Overview

Brought to you by YData

Dataset statistics

Number of variables51
Number of observations84
Missing cells1728
Missing cells (%)40.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.6 KiB
Average record size in memory409.6 B

Variable types

Numeric22
Categorical20
Boolean4
Unsupported5

Alerts

Process (PMF) - Prototyping Used has constant value "True" Constant
Tech (TF) - Web Development has constant value "Web" Constant
Tech (TF) - DBMS Used has constant value "True" Constant
- CASE Tool Used is highly overall correlated with External (EEF) - Industry Sector and 6 other fieldsHigh correlation
External (EEF) - Data Quality Rating is highly overall correlated with External (EEF) - Organisation Type and 17 other fieldsHigh correlation
External (EEF) - Industry Sector is highly overall correlated with - CASE Tool Used and 17 other fieldsHigh correlation
External (EEF) - Organisation Type is highly overall correlated with External (EEF) - Data Quality Rating and 31 other fieldsHigh correlation
People (PRF) - BA team experience 1 to 3 yr is highly overall correlated with External (EEF) - Organisation Type and 10 other fieldsHigh correlation
People (PRF) - BA team experience <1 yr is highly overall correlated with People (PRF) - IT experience <3 yr and 5 other fieldsHigh correlation
People (PRF) - BA team experience >3 yr is highly overall correlated with External (EEF) - Organisation Type and 7 other fieldsHigh correlation
People (PRF) - IT experience 3 to 9 yr is highly overall correlated with External (EEF) - Data Quality Rating and 11 other fieldsHigh correlation
People (PRF) - IT experience <3 yr is highly overall correlated with People (PRF) - BA team experience <1 yr and 5 other fieldsHigh correlation
People (PRF) - IT experience >9 yr is highly overall correlated with External (EEF) - Organisation Type and 10 other fieldsHigh correlation
People (PRF) - Personnel changes is highly overall correlated with External (EEF) - Organisation Type and 16 other fieldsHigh correlation
People (PRF) - Project manage changes is highly overall correlated with External (EEF) - Organisation Type and 11 other fieldsHigh correlation
People (PRF) - Project manage experience is highly overall correlated with External (EEF) - Industry Sector and 10 other fieldsHigh correlation
Process (PMF) - Development Methodologies is highly overall correlated with External (EEF) - Data Quality Rating and 23 other fieldsHigh correlation
Process (PMF) - Docs is highly overall correlated with External (EEF) - Data Quality Rating and 14 other fieldsHigh correlation
Project (PRF) - Application Group is highly overall correlated with External (EEF) - Industry Sector and 7 other fieldsHigh correlation
Project (PRF) - Application Type is highly overall correlated with External (EEF) - Data Quality Rating and 31 other fieldsHigh correlation
Project (PRF) - Cost currency is highly overall correlated with External (EEF) - Data Quality Rating and 27 other fieldsHigh correlation
Project (PRF) - Currency multiple is highly overall correlated with - CASE Tool Used and 22 other fieldsHigh correlation
Project (PRF) - Defect Density is highly overall correlated with External (EEF) - Organisation Type and 6 other fieldsHigh correlation
Project (PRF) - Development Type is highly overall correlated with External (EEF) - Industry Sector and 11 other fieldsHigh correlation
Project (PRF) - Functional Size is highly overall correlated with External (EEF) - Data Quality Rating and 15 other fieldsHigh correlation
Project (PRF) - Manpower Delivery Rate is highly overall correlated with External (EEF) - Data Quality Rating and 12 other fieldsHigh correlation
Project (PRF) - Max Team Size is highly overall correlated with External (EEF) - Data Quality Rating and 19 other fieldsHigh correlation
Project (PRF) - Normalised Level 1 PDR (ufp) is highly overall correlated with Project (PRF) - Max Team Size and 7 other fieldsHigh correlation
Project (PRF) - Normalised PDR (ufp) is highly overall correlated with Project (PRF) - Max Team Size and 7 other fieldsHigh correlation
Project (PRF) - Normalised Work Effort is highly overall correlated with External (EEF) - Data Quality Rating and 15 other fieldsHigh correlation
Project (PRF) - Normalised Work Effort Level 1 is highly overall correlated with External (EEF) - Organisation Type and 16 other fieldsHigh correlation
Project (PRF) - Project Elapsed Time is highly overall correlated with External (EEF) - Industry Sector and 16 other fieldsHigh correlation
Project (PRF) - Relative Size is highly overall correlated with External (EEF) - Data Quality Rating and 17 other fieldsHigh correlation
Project (PRF) - Speed of Delivery is highly overall correlated with External (EEF) - Data Quality Rating and 12 other fieldsHigh correlation
Project (PRF) - Team Size Group is highly overall correlated with External (EEF) - Data Quality Rating and 27 other fieldsHigh correlation
Project (PRF) - Total project cost is highly overall correlated with - CASE Tool Used and 27 other fieldsHigh correlation
Project (PRF) - Year of Project is highly overall correlated with - CASE Tool Used and 18 other fieldsHigh correlation
Tech (TF) - Architecture is highly overall correlated with External (EEF) - Industry Sector and 11 other fieldsHigh correlation
Tech (TF) - Client Roles is highly overall correlated with External (EEF) - Data Quality Rating and 30 other fieldsHigh correlation
Tech (TF) - Client Server? is highly overall correlated with External (EEF) - Data Quality Rating and 10 other fieldsHigh correlation
Tech (TF) - Development Platform is highly overall correlated with - CASE Tool Used and 15 other fieldsHigh correlation
Tech (TF) - Language Type is highly overall correlated with External (EEF) - Industry Sector and 15 other fieldsHigh correlation
Tech (TF) - Primary Programming Language is highly overall correlated with External (EEF) - Organisation Type and 11 other fieldsHigh correlation
Tech (TF) - Server Roles is highly overall correlated with - CASE Tool Used and 26 other fieldsHigh correlation
Tech (TF) - Tools Used is highly overall correlated with - CASE Tool Used and 17 other fieldsHigh correlation
Project (PRF) - Application Group is highly imbalanced (78.2%) Imbalance
Process (PMF) - Development Methodologies is highly imbalanced (59.8%) Imbalance
People (PRF) - Project manage changes is highly imbalanced (53.4%) Imbalance
External (EEF) - Industry Sector has 1 (1.2%) missing values Missing
Project (PRF) - Application Group has 5 (6.0%) missing values Missing
Tech (TF) - Development Platform has 15 (17.9%) missing values Missing
Project (PRF) - Functional Size has 1 (1.2%) missing values Missing
Project (PRF) - Relative Size has 1 (1.2%) missing values Missing
Project (PRF) - Normalised Level 1 PDR (ufp) has 1 (1.2%) missing values Missing
Project (PRF) - Normalised PDR (ufp) has 1 (1.2%) missing values Missing
Project (PRF) - Defect Density has 53 (63.1%) missing values Missing
Project (PRF) - Speed of Delivery has 3 (3.6%) missing values Missing
Project (PRF) - Manpower Delivery Rate has 28 (33.3%) missing values Missing
Project (PRF) - Project Elapsed Time has 2 (2.4%) missing values Missing
Project (PRF) - Team Size Group has 25 (29.8%) missing values Missing
Project (PRF) - Max Team Size has 25 (29.8%) missing values Missing
- CASE Tool Used has 68 (81.0%) missing values Missing
Process (PMF) - Development Methodologies has 4 (4.8%) missing values Missing
Process (PMF) - Prototyping Used has 79 (94.0%) missing values Missing
Tech (TF) - Architecture has 15 (17.9%) missing values Missing
Tech (TF) - Client Server? has 52 (61.9%) missing values Missing
Tech (TF) - Client Roles has 58 (69.0%) missing values Missing
Tech (TF) - Server Roles has 57 (67.9%) missing values Missing
Tech (TF) - Type of Server has 84 (100.0%) missing values Missing
Tech (TF) - Web Development has 50 (59.5%) missing values Missing
Tech (TF) - DBMS Used has 27 (32.1%) missing values Missing
People (PRF) - Project user involvement has 84 (100.0%) missing values Missing
People (PRF) - BA team experience <1 yr has 69 (82.1%) missing values Missing
People (PRF) - BA team experience 1 to 3 yr has 66 (78.6%) missing values Missing
People (PRF) - BA team experience >3 yr has 67 (79.8%) missing values Missing
People (PRF) - IT experience <1 yr has 84 (100.0%) missing values Missing
People (PRF) - IT experience 1 to 3 yr has 84 (100.0%) missing values Missing
People (PRF) - IT experience >3 yr has 84 (100.0%) missing values Missing
People (PRF) - IT experience <3 yr has 61 (72.6%) missing values Missing
People (PRF) - IT experience 3 to 9 yr has 63 (75.0%) missing values Missing
People (PRF) - IT experience >9 yr has 67 (79.8%) missing values Missing
People (PRF) - Project manage experience has 65 (77.4%) missing values Missing
People (PRF) - Project manage changes has 52 (61.9%) missing values Missing
People (PRF) - Personnel changes has 52 (61.9%) missing values Missing
Project (PRF) - Total project cost has 55 (65.5%) missing values Missing
Project (PRF) - Cost currency has 54 (64.3%) missing values Missing
Project (PRF) - Currency multiple has 66 (78.6%) missing values Missing
Tech (TF) - Type of Server is an unsupported type, check if it needs cleaning or further analysis Unsupported
People (PRF) - Project user involvement is an unsupported type, check if it needs cleaning or further analysis Unsupported
People (PRF) - IT experience <1 yr is an unsupported type, check if it needs cleaning or further analysis Unsupported
People (PRF) - IT experience 1 to 3 yr is an unsupported type, check if it needs cleaning or further analysis Unsupported
People (PRF) - IT experience >3 yr is an unsupported type, check if it needs cleaning or further analysis Unsupported
Project (PRF) - Defect Density has 18 (21.4%) zeros Zeros
Tech (TF) - Tools Used has 28 (33.3%) zeros Zeros
People (PRF) - BA team experience <1 yr has 7 (8.3%) zeros Zeros
People (PRF) - BA team experience 1 to 3 yr has 6 (7.1%) zeros Zeros
People (PRF) - BA team experience >3 yr has 3 (3.6%) zeros Zeros
People (PRF) - IT experience <3 yr has 6 (7.1%) zeros Zeros
People (PRF) - IT experience 3 to 9 yr has 3 (3.6%) zeros Zeros
People (PRF) - IT experience >9 yr has 2 (2.4%) zeros Zeros

Reproduction

Analysis started2025-05-15 13:26:26.986562
Analysis finished2025-05-15 13:28:04.962394
Duration1 minute and 37.98 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ISBSG Project ID
Real number (ℝ)

Distinct83
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20951.143
Minimum10279
Maximum32010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:05.147135image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10279
5-th percentile11498.8
Q114851.25
median20162
Q326671.25
95-th percentile31036.25
Maximum32010
Range21731
Interquartile range (IQR)11820

Descriptive statistics

Standard deviation6595.8526
Coefficient of variation (CV)0.31482066
Kurtosis-1.3453905
Mean20951.143
Median Absolute Deviation (MAD)5832
Skewness0.078118411
Sum1759896
Variance43505271
MonotonicityIncreasing
2025-05-15T14:28:05.394250image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31166 2
 
2.4%
10279 1
 
1.2%
24701 1
 
1.2%
26422 1
 
1.2%
26195 1
 
1.2%
26034 1
 
1.2%
25559 1
 
1.2%
25480 1
 
1.2%
25415 1
 
1.2%
25247 1
 
1.2%
Other values (73) 73
86.9%
ValueCountFrequency (%)
10279 1
1.2%
10317 1
1.2%
10572 1
1.2%
11278 1
1.2%
11497 1
1.2%
11509 1
1.2%
11738 1
1.2%
11801 1
1.2%
12664 1
1.2%
13026 1
1.2%
ValueCountFrequency (%)
32010 1
1.2%
31969 1
1.2%
31166 2
2.4%
31103 1
1.2%
30658 1
1.2%
30621 1
1.2%
30466 1
1.2%
30367 1
1.2%
30029 1
1.2%
29471 1
1.2%

External (EEF) - Data Quality Rating
Categorical

High correlation 

Distinct4
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size804.0 B
B
57 
A
25 
C
 
1
D
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
B 57
67.9%
A 25
29.8%
C 1
 
1.2%
D 1
 
1.2%

Length

2025-05-15T14:28:05.661469image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:05.836179image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
b 57
67.9%
a 25
29.8%
c 1
 
1.2%
d 1
 
1.2%

Most occurring characters

ValueCountFrequency (%)
B 57
67.9%
A 25
29.8%
C 1
 
1.2%
D 1
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 57
67.9%
A 25
29.8%
C 1
 
1.2%
D 1
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 57
67.9%
A 25
29.8%
C 1
 
1.2%
D 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 57
67.9%
A 25
29.8%
C 1
 
1.2%
D 1
 
1.2%

Project (PRF) - Year of Project
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.369
Minimum2005
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:05.978266image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2008.15
Q12009.75
median2012
Q32013
95-th percentile2014
Maximum2015
Range10
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.166446
Coefficient of variation (CV)0.0010771002
Kurtosis-0.67937714
Mean2011.369
Median Absolute Deviation (MAD)2
Skewness-0.20697571
Sum168955
Variance4.6934882
MonotonicityNot monotonic
2025-05-15T14:28:06.135207image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2009 16
19.0%
2010 15
17.9%
2013 14
16.7%
2014 13
15.5%
2012 13
15.5%
2015 4
 
4.8%
2011 4
 
4.8%
2008 4
 
4.8%
2005 1
 
1.2%
ValueCountFrequency (%)
2005 1
 
1.2%
2008 4
 
4.8%
2009 16
19.0%
2010 15
17.9%
2011 4
 
4.8%
2012 13
15.5%
2013 14
16.7%
2014 13
15.5%
2015 4
 
4.8%
ValueCountFrequency (%)
2015 4
 
4.8%
2014 13
15.5%
2013 14
16.7%
2012 13
15.5%
2011 4
 
4.8%
2010 15
17.9%
2009 16
19.0%
2008 4
 
4.8%
2005 1
 
1.2%

External (EEF) - Industry Sector
Categorical

High correlation  Missing 

Distinct10
Distinct (%)12.0%
Missing1
Missing (%)1.2%
Memory size804.0 B
Banking
24 
Government
20 
Service Industry
14 
Education
13 
Manufacturing
Other values (5)

Length

Max length23
Median length21
Mean length11.084337
Min length7

Characters and Unicode

Total characters920
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)3.6%

Sample

1st rowBanking
2nd rowGovernment
3rd rowGovernment
4th rowService Industry
5th rowBanking

Common Values

ValueCountFrequency (%)
Banking 24
28.6%
Government 20
23.8%
Service Industry 14
16.7%
Education 13
15.5%
Manufacturing 3
 
3.6%
Medical & Health Care 3
 
3.6%
Electronics & Computers 3
 
3.6%
Communication 1
 
1.2%
Financial 1
 
1.2%
Wholesale & Retail 1
 
1.2%
(Missing) 1
 
1.2%

Length

2025-05-15T14:28:06.352195image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:06.589736image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
banking 24
21.1%
government 20
17.5%
service 14
12.3%
industry 14
12.3%
education 13
11.4%
7
 
6.1%
manufacturing 3
 
2.6%
medical 3
 
2.6%
health 3
 
2.6%
care 3
 
2.6%
Other values (6) 10
8.8%

Most occurring characters

ValueCountFrequency (%)
n 128
13.9%
e 86
 
9.3%
i 65
 
7.1%
t 61
 
6.6%
r 60
 
6.5%
a 57
 
6.2%
o 42
 
4.6%
c 41
 
4.5%
u 37
 
4.0%
v 34
 
3.7%
Other values (23) 309
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 128
13.9%
e 86
 
9.3%
i 65
 
7.1%
t 61
 
6.6%
r 60
 
6.5%
a 57
 
6.2%
o 42
 
4.6%
c 41
 
4.5%
u 37
 
4.0%
v 34
 
3.7%
Other values (23) 309
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 128
13.9%
e 86
 
9.3%
i 65
 
7.1%
t 61
 
6.6%
r 60
 
6.5%
a 57
 
6.2%
o 42
 
4.6%
c 41
 
4.5%
u 37
 
4.0%
v 34
 
3.7%
Other values (23) 309
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 128
13.9%
e 86
 
9.3%
i 65
 
7.1%
t 61
 
6.6%
r 60
 
6.5%
a 57
 
6.2%
o 42
 
4.6%
c 41
 
4.5%
u 37
 
4.0%
v 34
 
3.7%
Other values (23) 309
33.6%

External (EEF) - Organisation Type
Categorical

High correlation 

Distinct25
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size804.0 B
Government;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;
23 
Government;
19 
Education Institution;Electricity, Gas, Water;IEEE;
Education Institution;Electricity, Gas, Water;University;
Art , Events , Ticketing;
Other values (20)
28 

Length

Max length254
Median length91
Mean length54.940476
Min length8

Characters and Unicode

Total characters4615
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)15.5%

Sample

1st rowGovernment;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;
2nd rowGovernment;
3rd rowGovernment;
4th rowCommunity Services;
5th rowGovernment;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;

Common Values

ValueCountFrequency (%)
Government;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking; 23
27.4%
Government; 19
22.6%
Education Institution;Electricity, Gas, Water;IEEE; 6
 
7.1%
Education Institution;Electricity, Gas, Water;University; 5
 
6.0%
Art , Events , Ticketing; 3
 
3.6%
Transport & Storage; 3
 
3.6%
Aerospace / Automotive; 2
 
2.4%
Community Services; 2
 
2.4%
Surveillance & Security; 2
 
2.4%
High Tech; 2
 
2.4%
Other values (15) 17
20.2%

Length

2025-05-15T14:28:06.814962image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
64
16.2%
and 26
 
6.6%
health 26
 
6.6%
retail 24
 
6.1%
trade;transport 24
 
6.1%
storage;communications;medical 24
 
6.1%
government;education 23
 
5.8%
care;banking 23
 
5.8%
institution;wholesale 23
 
5.8%
government 19
 
4.8%
Other values (52) 120
30.3%

Most occurring characters

ValueCountFrequency (%)
t 401
 
8.7%
n 399
 
8.6%
e 376
 
8.1%
a 358
 
7.8%
i 319
 
6.9%
312
 
6.8%
o 282
 
6.1%
; 260
 
5.6%
r 238
 
5.2%
s 162
 
3.5%
Other values (35) 1508
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 401
 
8.7%
n 399
 
8.6%
e 376
 
8.1%
a 358
 
7.8%
i 319
 
6.9%
312
 
6.8%
o 282
 
6.1%
; 260
 
5.6%
r 238
 
5.2%
s 162
 
3.5%
Other values (35) 1508
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 401
 
8.7%
n 399
 
8.6%
e 376
 
8.1%
a 358
 
7.8%
i 319
 
6.9%
312
 
6.8%
o 282
 
6.1%
; 260
 
5.6%
r 238
 
5.2%
s 162
 
3.5%
Other values (35) 1508
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 401
 
8.7%
n 399
 
8.6%
e 376
 
8.1%
a 358
 
7.8%
i 319
 
6.9%
312
 
6.8%
o 282
 
6.1%
; 260
 
5.6%
r 238
 
5.2%
s 162
 
3.5%
Other values (35) 1508
32.7%

Project (PRF) - Application Group
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)5.1%
Missing5
Missing (%)6.0%
Memory size804.0 B
Business Application
74 
Real-Time Application
 
2
Infrastructure Software
 
2
Mathematically-Intensive Application
 
1

Length

Max length36
Median length20
Mean length20.303797
Min length20

Characters and Unicode

Total characters1604
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st rowBusiness Application
2nd rowBusiness Application
3rd rowBusiness Application
4th rowBusiness Application
5th rowBusiness Application

Common Values

ValueCountFrequency (%)
Business Application 74
88.1%
Real-Time Application 2
 
2.4%
Infrastructure Software 2
 
2.4%
Mathematically-Intensive Application 1
 
1.2%
(Missing) 5
 
6.0%

Length

2025-05-15T14:28:07.051267image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:07.304470image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
application 77
48.7%
business 74
46.8%
real-time 2
 
1.3%
infrastructure 2
 
1.3%
software 2
 
1.3%
mathematically-intensive 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
i 232
14.5%
s 225
14.0%
n 155
9.7%
p 154
9.6%
a 86
 
5.4%
t 86
 
5.4%
e 85
 
5.3%
l 81
 
5.0%
c 80
 
5.0%
o 79
 
4.9%
Other values (17) 341
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 232
14.5%
s 225
14.0%
n 155
9.7%
p 154
9.6%
a 86
 
5.4%
t 86
 
5.4%
e 85
 
5.3%
l 81
 
5.0%
c 80
 
5.0%
o 79
 
4.9%
Other values (17) 341
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 232
14.5%
s 225
14.0%
n 155
9.7%
p 154
9.6%
a 86
 
5.4%
t 86
 
5.4%
e 85
 
5.3%
l 81
 
5.0%
c 80
 
5.0%
o 79
 
4.9%
Other values (17) 341
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 232
14.5%
s 225
14.0%
n 155
9.7%
p 154
9.6%
a 86
 
5.4%
t 86
 
5.4%
e 85
 
5.3%
l 81
 
5.0%
c 80
 
5.0%
o 79
 
4.9%
Other values (17) 341
21.3%

Project (PRF) - Application Type
Categorical

High correlation 

Distinct32
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Memory size804.0 B
Surveillance and security;
23 
Business Application;
14 
Content management system;Dynamic website;
Course management system;Dynamic website;
Document management;Financial transaction process/accounting;Online analysis and reporting;Stock control & order processing;Workflow support & management;Customer billing;Reservation system (eg. Airline, hotel);
 
3
Other values (27)
33 

Length

Max length255
Median length101
Mean length39.595238
Min length4

Characters and Unicode

Total characters3326
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)25.0%

Sample

1st rowSurveillance and security;
2nd rowBusiness Application;
3rd rowBusiness Application;
4th rowWorkflow support & management;Complex process control;
5th rowSurveillance and security;

Common Values

ValueCountFrequency (%)
Surveillance and security; 23
27.4%
Business Application; 14
16.7%
Content management system;Dynamic website; 6
 
7.1%
Course management system;Dynamic website; 5
 
6.0%
Document management;Financial transaction process/accounting;Online analysis and reporting;Stock control & order processing;Workflow support & management;Customer billing;Reservation system (eg. Airline, hotel); 3
 
3.6%
Data or database management; 2
 
2.4%
Workflow support & management;Complex process control; 2
 
2.4%
Electronic Data Interchange; 2
 
2.4%
IdM; 2
 
2.4%
Catalogue/register of things or events; 2
 
2.4%
Other values (22) 23
27.4%

Length

2025-05-15T14:28:08.162412image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 29
 
8.6%
security 24
 
7.1%
surveillance 23
 
6.8%
management 22
 
6.5%
application 15
 
4.5%
business 14
 
4.2%
12
 
3.6%
system;dynamic 11
 
3.3%
website 11
 
3.3%
or 10
 
3.0%
Other values (80) 166
49.3%

Most occurring characters

ValueCountFrequency (%)
e 322
 
9.7%
n 301
 
9.0%
253
 
7.6%
a 243
 
7.3%
t 227
 
6.8%
i 225
 
6.8%
s 198
 
6.0%
r 166
 
5.0%
o 166
 
5.0%
c 144
 
4.3%
Other values (41) 1081
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 322
 
9.7%
n 301
 
9.0%
253
 
7.6%
a 243
 
7.3%
t 227
 
6.8%
i 225
 
6.8%
s 198
 
6.0%
r 166
 
5.0%
o 166
 
5.0%
c 144
 
4.3%
Other values (41) 1081
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 322
 
9.7%
n 301
 
9.0%
253
 
7.6%
a 243
 
7.3%
t 227
 
6.8%
i 225
 
6.8%
s 198
 
6.0%
r 166
 
5.0%
o 166
 
5.0%
c 144
 
4.3%
Other values (41) 1081
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 322
 
9.7%
n 301
 
9.0%
253
 
7.6%
a 243
 
7.3%
t 227
 
6.8%
i 225
 
6.8%
s 198
 
6.0%
r 166
 
5.0%
o 166
 
5.0%
c 144
 
4.3%
Other values (41) 1081
32.5%

Project (PRF) - Development Type
Categorical

High correlation 

Distinct3
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size804.0 B
Enhancement
48 
New Development
32 
Re-development
 
4

Length

Max length15
Median length11
Mean length12.666667
Min length11

Characters and Unicode

Total characters1064
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnhancement
2nd rowEnhancement
3rd rowEnhancement
4th rowEnhancement
5th rowEnhancement

Common Values

ValueCountFrequency (%)
Enhancement 48
57.1%
New Development 32
38.1%
Re-development 4
 
4.8%

Length

2025-05-15T14:28:08.393260image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:08.542933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
enhancement 48
41.4%
new 32
27.6%
development 32
27.6%
re-development 4
 
3.4%

Most occurring characters

ValueCountFrequency (%)
e 240
22.6%
n 180
16.9%
m 84
 
7.9%
t 84
 
7.9%
E 48
 
4.5%
h 48
 
4.5%
a 48
 
4.5%
c 48
 
4.5%
v 36
 
3.4%
p 36
 
3.4%
Other values (9) 212
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 240
22.6%
n 180
16.9%
m 84
 
7.9%
t 84
 
7.9%
E 48
 
4.5%
h 48
 
4.5%
a 48
 
4.5%
c 48
 
4.5%
v 36
 
3.4%
p 36
 
3.4%
Other values (9) 212
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 240
22.6%
n 180
16.9%
m 84
 
7.9%
t 84
 
7.9%
E 48
 
4.5%
h 48
 
4.5%
a 48
 
4.5%
c 48
 
4.5%
v 36
 
3.4%
p 36
 
3.4%
Other values (9) 212
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 240
22.6%
n 180
16.9%
m 84
 
7.9%
t 84
 
7.9%
E 48
 
4.5%
h 48
 
4.5%
a 48
 
4.5%
c 48
 
4.5%
v 36
 
3.4%
p 36
 
3.4%
Other values (9) 212
19.9%

Tech (TF) - Development Platform
Categorical

High correlation  Missing 

Distinct4
Distinct (%)5.8%
Missing15
Missing (%)17.9%
Memory size804.0 B
PC
49 
Proprietary
10 
Multi
MR
 
1

Length

Max length11
Median length2
Mean length3.6956522
Min length2

Characters and Unicode

Total characters255
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st rowPC
2nd rowMulti
3rd rowPC
4th rowMulti
5th rowPC

Common Values

ValueCountFrequency (%)
PC 49
58.3%
Proprietary 10
 
11.9%
Multi 9
 
10.7%
MR 1
 
1.2%
(Missing) 15
 
17.9%

Length

2025-05-15T14:28:08.827775image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:09.027790image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
pc 49
71.0%
proprietary 10
 
14.5%
multi 9
 
13.0%
mr 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
P 59
23.1%
C 49
19.2%
r 30
11.8%
i 19
 
7.5%
t 19
 
7.5%
o 10
 
3.9%
p 10
 
3.9%
e 10
 
3.9%
a 10
 
3.9%
y 10
 
3.9%
Other values (4) 29
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 59
23.1%
C 49
19.2%
r 30
11.8%
i 19
 
7.5%
t 19
 
7.5%
o 10
 
3.9%
p 10
 
3.9%
e 10
 
3.9%
a 10
 
3.9%
y 10
 
3.9%
Other values (4) 29
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 59
23.1%
C 49
19.2%
r 30
11.8%
i 19
 
7.5%
t 19
 
7.5%
o 10
 
3.9%
p 10
 
3.9%
e 10
 
3.9%
a 10
 
3.9%
y 10
 
3.9%
Other values (4) 29
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 59
23.1%
C 49
19.2%
r 30
11.8%
i 19
 
7.5%
t 19
 
7.5%
o 10
 
3.9%
p 10
 
3.9%
e 10
 
3.9%
a 10
 
3.9%
y 10
 
3.9%
Other values (4) 29
11.4%

Tech (TF) - Language Type
Categorical

High correlation 

Distinct3
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size804.0 B
3GL
56 
4GL
18 
5GL
10 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters252
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3GL
2nd row4GL
3rd row4GL
4th row3GL
5th row3GL

Common Values

ValueCountFrequency (%)
3GL 56
66.7%
4GL 18
 
21.4%
5GL 10
 
11.9%

Length

2025-05-15T14:28:09.205873image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:09.445492image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3gl 56
66.7%
4gl 18
 
21.4%
5gl 10
 
11.9%

Most occurring characters

ValueCountFrequency (%)
G 84
33.3%
L 84
33.3%
3 56
22.2%
4 18
 
7.1%
5 10
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 84
33.3%
L 84
33.3%
3 56
22.2%
4 18
 
7.1%
5 10
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 84
33.3%
L 84
33.3%
3 56
22.2%
4 18
 
7.1%
5 10
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 84
33.3%
L 84
33.3%
3 56
22.2%
4 18
 
7.1%
5 10
 
4.0%

Tech (TF) - Primary Programming Language
Categorical

High correlation 

Distinct9
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size804.0 B
C#
35 
Java
16 
Proprietary Agile Platform
10 
Oracle
.Net
Other values (4)

Length

Max length26
Median length10
Mean length6.1190476
Min length2

Characters and Unicode

Total characters514
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st rowC#
2nd row.Net
3rd rowOracle
4th rowJavaScript
5th rowC#

Common Values

ValueCountFrequency (%)
C# 35
41.7%
Java 16
19.0%
Proprietary Agile Platform 10
 
11.9%
Oracle 9
 
10.7%
.Net 8
 
9.5%
JavaScript 2
 
2.4%
C++ 2
 
2.4%
ABAP 1
 
1.2%
PL/I 1
 
1.2%

Length

2025-05-15T14:28:09.702007image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:09.926768image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
c 37
35.6%
java 16
15.4%
proprietary 10
 
9.6%
agile 10
 
9.6%
platform 10
 
9.6%
oracle 9
 
8.7%
net 8
 
7.7%
javascript 2
 
1.9%
abap 1
 
1.0%
pl/i 1
 
1.0%

Most occurring characters

ValueCountFrequency (%)
a 65
 
12.6%
r 51
 
9.9%
C 37
 
7.2%
e 37
 
7.2%
# 35
 
6.8%
t 30
 
5.8%
l 29
 
5.6%
P 22
 
4.3%
i 22
 
4.3%
20
 
3.9%
Other values (19) 166
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 514
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 65
 
12.6%
r 51
 
9.9%
C 37
 
7.2%
e 37
 
7.2%
# 35
 
6.8%
t 30
 
5.8%
l 29
 
5.6%
P 22
 
4.3%
i 22
 
4.3%
20
 
3.9%
Other values (19) 166
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 514
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 65
 
12.6%
r 51
 
9.9%
C 37
 
7.2%
e 37
 
7.2%
# 35
 
6.8%
t 30
 
5.8%
l 29
 
5.6%
P 22
 
4.3%
i 22
 
4.3%
20
 
3.9%
Other values (19) 166
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 514
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 65
 
12.6%
r 51
 
9.9%
C 37
 
7.2%
e 37
 
7.2%
# 35
 
6.8%
t 30
 
5.8%
l 29
 
5.6%
P 22
 
4.3%
i 22
 
4.3%
20
 
3.9%
Other values (19) 166
32.3%

Project (PRF) - Functional Size
Real number (ℝ)

High correlation  Missing 

Distinct75
Distinct (%)90.4%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean240.3494
Minimum2
Maximum5393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:10.192096image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8.1
Q124.5
median85
Q3172.5
95-th percentile742.7
Maximum5393
Range5391
Interquartile range (IQR)148

Descriptive statistics

Standard deviation643.22396
Coefficient of variation (CV)2.6762037
Kurtosis51.735933
Mean240.3494
Median Absolute Deviation (MAD)67
Skewness6.7233306
Sum19949
Variance413737.06
MonotonicityNot monotonic
2025-05-15T14:28:10.479687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
243 2
 
2.4%
138 2
 
2.4%
72 2
 
2.4%
45 2
 
2.4%
13 2
 
2.4%
18 2
 
2.4%
51 2
 
2.4%
15 2
 
2.4%
155 1
 
1.2%
113 1
 
1.2%
Other values (65) 65
77.4%
ValueCountFrequency (%)
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
11 1
1.2%
12 1
1.2%
13 2
2.4%
ValueCountFrequency (%)
5393 1
1.2%
2003 1
1.2%
1107 1
1.2%
912 1
1.2%
748 1
1.2%
695 1
1.2%
679 1
1.2%
498 1
1.2%
492 1
1.2%
483 1
1.2%

Project (PRF) - Relative Size
Categorical

High correlation  Missing 

Distinct7
Distinct (%)8.4%
Missing1
Missing (%)1.2%
Memory size804.0 B
M1
24 
S
23 
XS
17 
M2
10 
XXS
Other values (2)

Length

Max length3
Median length2
Mean length1.7710843
Min length1

Characters and Unicode

Total characters147
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowXS
2nd rowXXS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
M1 24
28.6%
S 23
27.4%
XS 17
20.2%
M2 10
11.9%
XXS 6
 
7.1%
L 2
 
2.4%
XL 1
 
1.2%
(Missing) 1
 
1.2%

Length

2025-05-15T14:28:10.702620image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:10.882401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
m1 24
28.9%
s 23
27.7%
xs 17
20.5%
m2 10
12.0%
xxs 6
 
7.2%
l 2
 
2.4%
xl 1
 
1.2%

Most occurring characters

ValueCountFrequency (%)
S 46
31.3%
M 34
23.1%
X 30
20.4%
1 24
16.3%
2 10
 
6.8%
L 3
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 46
31.3%
M 34
23.1%
X 30
20.4%
1 24
16.3%
2 10
 
6.8%
L 3
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 46
31.3%
M 34
23.1%
X 30
20.4%
1 24
16.3%
2 10
 
6.8%
L 3
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 46
31.3%
M 34
23.1%
X 30
20.4%
1 24
16.3%
2 10
 
6.8%
L 3
 
2.0%

Project (PRF) - Normalised Work Effort Level 1
Real number (ℝ)

High correlation 

Distinct75
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2935.7738
Minimum6
Maximum52743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:11.113963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20.45
Q177.75
median470.5
Q31020
95-th percentile17835.85
Maximum52743
Range52737
Interquartile range (IQR)942.25

Descriptive statistics

Standard deviation8964.9271
Coefficient of variation (CV)3.0536846
Kurtosis20.02953
Mean2935.7738
Median Absolute Deviation (MAD)419
Skewness4.4292196
Sum246605
Variance80369919
MonotonicityNot monotonic
2025-05-15T14:28:11.356160image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 4
 
4.8%
20 3
 
3.6%
1020 2
 
2.4%
225 2
 
2.4%
125 2
 
2.4%
51 2
 
2.4%
52743 1
 
1.2%
98 1
 
1.2%
606 1
 
1.2%
326 1
 
1.2%
Other values (65) 65
77.4%
ValueCountFrequency (%)
6 1
 
1.2%
9 1
 
1.2%
20 3
3.6%
23 1
 
1.2%
27 1
 
1.2%
29 1
 
1.2%
34 1
 
1.2%
38 1
 
1.2%
43 1
 
1.2%
47 4
4.8%
ValueCountFrequency (%)
52743 1
1.2%
47493 1
1.2%
36593 1
1.2%
19795 1
1.2%
19606 1
1.2%
7805 1
1.2%
7037 1
1.2%
5408 1
1.2%
4393 1
1.2%
4110 1
1.2%

Project (PRF) - Normalised Work Effort
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3219.6786
Minimum6
Maximum60047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:11.688410image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile28.15
Q185.75
median474.5
Q31060.75
95-th percentile17835.85
Maximum60047
Range60041
Interquartile range (IQR)975

Descriptive statistics

Standard deviation10291.495
Coefficient of variation (CV)3.1964356
Kurtosis20.485858
Mean3219.6786
Median Absolute Deviation (MAD)411
Skewness4.5205055
Sum270453
Variance1.0591487 × 108
MonotonicityNot monotonic
2025-05-15T14:28:11.892317image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 4
 
4.8%
225 2
 
2.4%
51 2
 
2.4%
1105 2
 
2.4%
125 2
 
2.4%
606 1
 
1.2%
326 1
 
1.2%
600 1
 
1.2%
667 1
 
1.2%
52743 1
 
1.2%
Other values (67) 67
79.8%
ValueCountFrequency (%)
6 1
 
1.2%
9 1
 
1.2%
23 1
 
1.2%
27 1
 
1.2%
28 1
 
1.2%
29 1
 
1.2%
34 1
 
1.2%
38 1
 
1.2%
43 1
 
1.2%
47 4
4.8%
ValueCountFrequency (%)
60047 1
1.2%
52743 1
1.2%
47493 1
1.2%
19795 1
1.2%
19606 1
1.2%
7805 1
1.2%
7037 1
1.2%
5408 1
1.2%
4393 1
1.2%
4110 1
1.2%

Project (PRF) - Normalised Level 1 PDR (ufp)
Real number (ℝ)

High correlation  Missing 

Distinct65
Distinct (%)78.3%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean12.912048
Minimum0.1
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:12.176476image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.51
Q12.35
median3.6
Q312.1
95-th percentile47.37
Maximum171
Range170.9
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation24.156448
Coefficient of variation (CV)1.8708456
Kurtosis23.753515
Mean12.912048
Median Absolute Deviation (MAD)2.1
Skewness4.3136469
Sum1071.7
Variance583.534
MonotonicityNot monotonic
2025-05-15T14:28:12.414315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 3
 
3.6%
3.5 3
 
3.6%
2 2
 
2.4%
3 2
 
2.4%
3.1 2
 
2.4%
2.1 2
 
2.4%
3.6 2
 
2.4%
4.4 2
 
2.4%
3.9 2
 
2.4%
2.6 2
 
2.4%
Other values (55) 61
72.6%
ValueCountFrequency (%)
0.1 1
1.2%
0.2 1
1.2%
0.4 2
2.4%
0.5 1
1.2%
0.6 1
1.2%
0.7 2
2.4%
0.8 1
1.2%
1 1
1.2%
1.1 1
1.2%
1.2 1
1.2%
ValueCountFrequency (%)
171 1
1.2%
102 1
1.2%
53.8 1
1.2%
48.2 1
1.2%
47.6 1
1.2%
45.3 1
1.2%
43.5 1
1.2%
39.7 1
1.2%
38.3 1
1.2%
30.3 1
1.2%

Project (PRF) - Normalised PDR (ufp)
Real number (ℝ)

High correlation  Missing 

Distinct64
Distinct (%)77.1%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean12.944578
Minimum0.1
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:12.721642image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.7
Q12.45
median3.6
Q312.1
95-th percentile47.37
Maximum171
Range170.9
Interquartile range (IQR)9.65

Descriptive statistics

Standard deviation24.140298
Coefficient of variation (CV)1.8648964
Kurtosis23.800333
Mean12.944578
Median Absolute Deviation (MAD)1.9
Skewness4.319178
Sum1074.4
Variance582.75396
MonotonicityNot monotonic
2025-05-15T14:28:13.005927image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 3
 
3.6%
2.6 3
 
3.6%
3.5 3
 
3.6%
2 2
 
2.4%
3 2
 
2.4%
3.1 2
 
2.4%
3.6 2
 
2.4%
4.4 2
 
2.4%
2.7 2
 
2.4%
1.9 2
 
2.4%
Other values (54) 60
71.4%
ValueCountFrequency (%)
0.1 1
1.2%
0.5 1
1.2%
0.6 2
2.4%
0.7 2
2.4%
0.8 1
1.2%
0.9 1
1.2%
1 1
1.2%
1.1 1
1.2%
1.2 1
1.2%
1.4 1
1.2%
ValueCountFrequency (%)
171 1
1.2%
102 1
1.2%
53.8 1
1.2%
48.2 1
1.2%
47.6 1
1.2%
45.3 1
1.2%
43.5 1
1.2%
39.7 1
1.2%
38.3 1
1.2%
30.3 1
1.2%

Project (PRF) - Defect Density
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct14
Distinct (%)45.2%
Missing53
Missing (%)63.1%
Infinite0
Infinite (%)0.0%
Mean19.229032
Minimum0
Maximum173.9
Zeros18
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:13.177389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q332.7
95-th percentile57.6
Maximum173.9
Range173.9
Interquartile range (IQR)32.7

Descriptive statistics

Standard deviation35.625732
Coefficient of variation (CV)1.8527054
Kurtosis11.410046
Mean19.229032
Median Absolute Deviation (MAD)0
Skewness2.9948561
Sum596.1
Variance1269.1928
MonotonicityNot monotonic
2025-05-15T14:28:13.335840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 18
 
21.4%
56.2 1
 
1.2%
47.6 1
 
1.2%
43.5 1
 
1.2%
59 1
 
1.2%
173.9 1
 
1.2%
21.3 1
 
1.2%
51.5 1
 
1.2%
42.8 1
 
1.2%
9.2 1
 
1.2%
Other values (4) 4
 
4.8%
(Missing) 53
63.1%
ValueCountFrequency (%)
0 18
21.4%
1.9 1
 
1.2%
9.2 1
 
1.2%
14 1
 
1.2%
21.3 1
 
1.2%
22.6 1
 
1.2%
42.8 1
 
1.2%
43.5 1
 
1.2%
47.6 1
 
1.2%
51.5 1
 
1.2%
ValueCountFrequency (%)
173.9 1
1.2%
59 1
1.2%
56.2 1
1.2%
52.6 1
1.2%
51.5 1
1.2%
47.6 1
1.2%
43.5 1
1.2%
42.8 1
1.2%
22.6 1
1.2%
21.3 1
1.2%

Project (PRF) - Speed of Delivery
Real number (ℝ)

High correlation  Missing 

Distinct75
Distinct (%)92.6%
Missing3
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean83.383951
Minimum0.2
Maximum2157.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:13.534402image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.8
Q13.1
median29.1
Q372.9
95-th percentile221.4
Maximum2157.2
Range2157
Interquartile range (IQR)69.8

Descriptive statistics

Standard deviation244.40772
Coefficient of variation (CV)2.9311122
Kurtosis66.758092
Mean83.383951
Median Absolute Deviation (MAD)27.5
Skewness7.8461854
Sum6754.1
Variance59735.132
MonotonicityNot monotonic
2025-05-15T14:28:13.880111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3 2
 
2.4%
0.8 2
 
2.4%
1.5 2
 
2.4%
1.1 2
 
2.4%
72.9 2
 
2.4%
121.5 2
 
2.4%
49.9 1
 
1.2%
51.7 1
 
1.2%
14.1 1
 
1.2%
74.3 1
 
1.2%
Other values (65) 65
77.4%
(Missing) 3
 
3.6%
ValueCountFrequency (%)
0.2 1
1.2%
0.3 1
1.2%
0.4 1
1.2%
0.8 2
2.4%
0.9 1
1.2%
1 1
1.2%
1.1 2
2.4%
1.2 1
1.2%
1.3 2
2.4%
1.5 2
2.4%
ValueCountFrequency (%)
2157.2 1
1.2%
345 1
1.2%
249.3 1
1.2%
237.5 1
1.2%
221.4 1
1.2%
220 1
1.2%
212.5 1
1.2%
200.3 1
1.2%
180 1
1.2%
169.8 1
1.2%

Project (PRF) - Manpower Delivery Rate
Real number (ℝ)

High correlation  Missing 

Distinct46
Distinct (%)82.1%
Missing28
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean46.051786
Minimum0.2
Maximum2157.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:14.066060image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.7
Q11.375
median2.95
Q39.575
95-th percentile35.725
Maximum2157.2
Range2157
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation287.59762
Coefficient of variation (CV)6.2450915
Kurtosis55.709157
Mean46.051786
Median Absolute Deviation (MAD)2
Skewness7.4551952
Sum2578.9
Variance82712.388
MonotonicityNot monotonic
2025-05-15T14:28:14.274500image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
9.5 3
 
3.6%
0.8 3
 
3.6%
1.3 3
 
3.6%
1.5 3
 
3.6%
2 2
 
2.4%
1.1 2
 
2.4%
82 1
 
1.2%
14.9 1
 
1.2%
4.5 1
 
1.2%
10.3 1
 
1.2%
Other values (36) 36
42.9%
(Missing) 28
33.3%
ValueCountFrequency (%)
0.2 1
 
1.2%
0.3 1
 
1.2%
0.4 1
 
1.2%
0.8 3
3.6%
0.9 1
 
1.2%
1 1
 
1.2%
1.1 2
2.4%
1.2 1
 
1.2%
1.3 3
3.6%
1.4 1
 
1.2%
ValueCountFrequency (%)
2157.2 1
1.2%
82 1
1.2%
70.3 1
1.2%
24.2 1
1.2%
16.2 1
1.2%
14.9 1
1.2%
13.9 1
1.2%
13.1 1
1.2%
11.8 1
1.2%
11.5 1
1.2%

Project (PRF) - Project Elapsed Time
Real number (ℝ)

High correlation  Missing 

Distinct21
Distinct (%)25.6%
Missing2
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean6.4207317
Minimum0.4
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:14.542419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.4
Q12.625
median4
Q312
95-th percentile12
Maximum19
Range18.6
Interquartile range (IQR)9.375

Descriptive statistics

Standard deviation5.0305668
Coefficient of variation (CV)0.78348808
Kurtosis-1.0914616
Mean6.4207317
Median Absolute Deviation (MAD)3.45
Skewness0.47166326
Sum526.5
Variance25.306602
MonotonicityNot monotonic
2025-05-15T14:28:14.745156image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
12 23
27.4%
3 19
22.6%
0.4 7
 
8.3%
5 4
 
4.8%
2 4
 
4.8%
0.7 4
 
4.8%
8 3
 
3.6%
9 2
 
2.4%
4 2
 
2.4%
10 2
 
2.4%
Other values (11) 12
14.3%
ValueCountFrequency (%)
0.4 7
 
8.3%
0.7 4
 
4.8%
1 1
 
1.2%
1.1 1
 
1.2%
1.4 1
 
1.2%
1.5 1
 
1.2%
1.6 1
 
1.2%
2 4
 
4.8%
2.5 1
 
1.2%
3 19
22.6%
ValueCountFrequency (%)
19 1
 
1.2%
18 1
 
1.2%
15 1
 
1.2%
14.8 1
 
1.2%
12 23
27.4%
10 2
 
2.4%
9 2
 
2.4%
8 3
 
3.6%
7 2
 
2.4%
5 4
 
4.8%

Project (PRF) - Team Size Group
Categorical

High correlation  Missing 

Distinct8
Distinct (%)13.6%
Missing25
Missing (%)29.8%
Memory size804.0 B
1
22 
5-8
12 
2
3-4
9-14
Other values (3)

Length

Max length5
Median length1
Mean length2.1864407
Min length1

Characters and Unicode

Total characters129
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.7%

Sample

1st row1
2nd row9-14
3rd row1
4th row5-8
5th row1

Common Values

ValueCountFrequency (%)
1 22
26.2%
5-8 12
14.3%
2 9
 
10.7%
3-4 7
 
8.3%
9-14 4
 
4.8%
21-30 2
 
2.4%
41-50 2
 
2.4%
61-70 1
 
1.2%
(Missing) 25
29.8%

Length

2025-05-15T14:28:15.005417image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:15.287369image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 22
37.3%
5-8 12
20.3%
2 9
15.3%
3-4 7
 
11.9%
9-14 4
 
6.8%
21-30 2
 
3.4%
41-50 2
 
3.4%
61-70 1
 
1.7%

Most occurring characters

ValueCountFrequency (%)
1 31
24.0%
- 28
21.7%
5 14
10.9%
4 13
10.1%
8 12
 
9.3%
2 11
 
8.5%
3 9
 
7.0%
0 5
 
3.9%
9 4
 
3.1%
6 1
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 31
24.0%
- 28
21.7%
5 14
10.9%
4 13
10.1%
8 12
 
9.3%
2 11
 
8.5%
3 9
 
7.0%
0 5
 
3.9%
9 4
 
3.1%
6 1
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 31
24.0%
- 28
21.7%
5 14
10.9%
4 13
10.1%
8 12
 
9.3%
2 11
 
8.5%
3 9
 
7.0%
0 5
 
3.9%
9 4
 
3.1%
6 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 31
24.0%
- 28
21.7%
5 14
10.9%
4 13
10.1%
8 12
 
9.3%
2 11
 
8.5%
3 9
 
7.0%
0 5
 
3.9%
9 4
 
3.1%
6 1
 
0.8%

Project (PRF) - Max Team Size
Real number (ℝ)

High correlation  Missing 

Distinct14
Distinct (%)23.7%
Missing25
Missing (%)29.8%
Infinite0
Infinite (%)0.0%
Mean6.3559322
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:15.476664image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile27.5
Maximum68
Range67
Interquartile range (IQR)4

Descriptive statistics

Standard deviation11.84201
Coefficient of variation (CV)1.8631429
Kurtosis14.906062
Mean6.3559322
Median Absolute Deviation (MAD)1
Skewness3.7127229
Sum375
Variance140.2332
MonotonicityNot monotonic
2025-05-15T14:28:15.688359image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 22
26.2%
5 10
 
11.9%
2 9
 
10.7%
4 5
 
6.0%
8 2
 
2.4%
3 2
 
2.4%
10 2
 
2.4%
9 1
 
1.2%
22 1
 
1.2%
26 1
 
1.2%
Other values (4) 4
 
4.8%
(Missing) 25
29.8%
ValueCountFrequency (%)
1 22
26.2%
2 9
10.7%
3 2
 
2.4%
4 5
 
6.0%
5 10
11.9%
8 2
 
2.4%
9 1
 
1.2%
10 2
 
2.4%
11 1
 
1.2%
22 1
 
1.2%
ValueCountFrequency (%)
68 1
 
1.2%
46 1
 
1.2%
41 1
 
1.2%
26 1
 
1.2%
22 1
 
1.2%
11 1
 
1.2%
10 2
 
2.4%
9 1
 
1.2%
8 2
 
2.4%
5 10
11.9%

- CASE Tool Used
Boolean

High correlation  Missing 

Distinct2
Distinct (%)12.5%
Missing68
Missing (%)81.0%
Memory size300.0 B
False
True
(Missing)
68 
ValueCountFrequency (%)
False 8
 
9.5%
True 8
 
9.5%
(Missing) 68
81.0%
2025-05-15T14:28:15.987425image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Process (PMF) - Development Methodologies
Categorical

High correlation  Imbalance  Missing 

Distinct9
Distinct (%)11.2%
Missing4
Missing (%)4.8%
Memory size804.0 B
Agile Development;
63 
Agile Development;Personal Software Process (PSP);Unified Process;
 
6
Personal Software Process (PSP);Unified Process;
 
5
Agile Development;Unified Process;
 
1
Agile Development;Scrum;
 
1
Other values (4)
 
4

Length

Max length131
Median length18
Mean length26.3625
Min length18

Characters and Unicode

Total characters2109
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)7.5%

Sample

1st rowAgile Development;
2nd rowAgile Development;
3rd rowAgile Development;
4th rowAgile Development;
5th rowAgile Development;

Common Values

ValueCountFrequency (%)
Agile Development; 63
75.0%
Agile Development;Personal Software Process (PSP);Unified Process; 6
 
7.1%
Personal Software Process (PSP);Unified Process; 5
 
6.0%
Agile Development;Unified Process; 1
 
1.2%
Agile Development;Scrum; 1
 
1.2%
Agile Development;Iterative; 1
 
1.2%
Agile Development;Multifunctional Teams;Scrum; 1
 
1.2%
Agile Development;Joint Application Development (JAD);Multifunctional Teams; 1
 
1.2%
Agile Development;Extreme Programming (XP);Iterative;Rapid Application Development (RAD);Scrum;Timeboxing;Unified Process;IMES OOM; 1
 
1.2%
(Missing) 4
 
4.8%

Length

2025-05-15T14:28:16.165805image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:16.490438image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
agile 75
35.4%
development 65
30.7%
process 23
 
10.8%
software 11
 
5.2%
psp);unified 11
 
5.2%
development;personal 6
 
2.8%
personal 5
 
2.4%
application 2
 
0.9%
teams 1
 
0.5%
process;imes 1
 
0.5%
Other values (12) 12
 
5.7%

Most occurring characters

ValueCountFrequency (%)
e 374
17.7%
l 169
 
8.0%
132
 
6.3%
o 130
 
6.2%
i 116
 
5.5%
; 111
 
5.3%
n 110
 
5.2%
t 100
 
4.7%
m 86
 
4.1%
p 82
 
3.9%
Other values (27) 699
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2109
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 374
17.7%
l 169
 
8.0%
132
 
6.3%
o 130
 
6.2%
i 116
 
5.5%
; 111
 
5.3%
n 110
 
5.2%
t 100
 
4.7%
m 86
 
4.1%
p 82
 
3.9%
Other values (27) 699
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2109
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 374
17.7%
l 169
 
8.0%
132
 
6.3%
o 130
 
6.2%
i 116
 
5.5%
; 111
 
5.3%
n 110
 
5.2%
t 100
 
4.7%
m 86
 
4.1%
p 82
 
3.9%
Other values (27) 699
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2109
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 374
17.7%
l 169
 
8.0%
132
 
6.3%
o 130
 
6.2%
i 116
 
5.5%
; 111
 
5.3%
n 110
 
5.2%
t 100
 
4.7%
m 86
 
4.1%
p 82
 
3.9%
Other values (27) 699
33.1%

Process (PMF) - Prototyping Used
Boolean

Constant  Missing 

Distinct1
Distinct (%)20.0%
Missing79
Missing (%)94.0%
Memory size300.0 B
True
 
5
(Missing)
79 
ValueCountFrequency (%)
True 5
 
6.0%
(Missing) 79
94.0%
2025-05-15T14:28:16.761234image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Process (PMF) - Docs
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5119048
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:16.901034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median6
Q317
95-th percentile18
Maximum20
Range19
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.0668843
Coefficient of variation (CV)0.63782013
Kurtosis-1.5381013
Mean9.5119048
Median Absolute Deviation (MAD)3
Skewness0.48262636
Sum799
Variance36.807085
MonotonicityNot monotonic
2025-05-15T14:28:17.097467image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
6 24
28.6%
18 15
17.9%
3 14
16.7%
5 10
11.9%
17 6
 
7.1%
16 4
 
4.8%
7 3
 
3.6%
15 2
 
2.4%
11 2
 
2.4%
14 1
 
1.2%
Other values (3) 3
 
3.6%
ValueCountFrequency (%)
1 1
 
1.2%
3 14
16.7%
5 10
11.9%
6 24
28.6%
7 3
 
3.6%
11 2
 
2.4%
14 1
 
1.2%
15 2
 
2.4%
16 4
 
4.8%
17 6
 
7.1%
ValueCountFrequency (%)
20 1
 
1.2%
19 1
 
1.2%
18 15
17.9%
17 6
 
7.1%
16 4
 
4.8%
15 2
 
2.4%
14 1
 
1.2%
11 2
 
2.4%
7 3
 
3.6%
6 24
28.6%

Tech (TF) - Architecture
Categorical

High correlation  Missing 

Distinct4
Distinct (%)5.8%
Missing15
Missing (%)17.9%
Memory size804.0 B
Stand alone
30 
Multi-tier with web public interface
27 
Client server
11 
Multi-tier
 
1

Length

Max length36
Median length13
Mean length21.086957
Min length10

Characters and Unicode

Total characters1455
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st rowStand alone
2nd rowClient server
3rd rowStand alone
4th rowMulti-tier
5th rowStand alone

Common Values

ValueCountFrequency (%)
Stand alone 30
35.7%
Multi-tier with web public interface 27
32.1%
Client server 11
 
13.1%
Multi-tier 1
 
1.2%
(Missing) 15
17.9%

Length

2025-05-15T14:28:17.412481image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:17.701628image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
stand 30
13.8%
alone 30
13.8%
multi-tier 28
12.8%
with 27
12.4%
web 27
12.4%
public 27
12.4%
interface 27
12.4%
client 11
 
5.0%
server 11
 
5.0%

Most occurring characters

ValueCountFrequency (%)
e 172
11.8%
t 151
10.4%
149
10.2%
i 148
10.2%
n 98
 
6.7%
l 96
 
6.6%
a 87
 
6.0%
r 77
 
5.3%
u 55
 
3.8%
c 54
 
3.7%
Other values (13) 368
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1455
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 172
11.8%
t 151
10.4%
149
10.2%
i 148
10.2%
n 98
 
6.7%
l 96
 
6.6%
a 87
 
6.0%
r 77
 
5.3%
u 55
 
3.8%
c 54
 
3.7%
Other values (13) 368
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1455
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 172
11.8%
t 151
10.4%
149
10.2%
i 148
10.2%
n 98
 
6.7%
l 96
 
6.6%
a 87
 
6.0%
r 77
 
5.3%
u 55
 
3.8%
c 54
 
3.7%
Other values (13) 368
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1455
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 172
11.8%
t 151
10.4%
149
10.2%
i 148
10.2%
n 98
 
6.7%
l 96
 
6.6%
a 87
 
6.0%
r 77
 
5.3%
u 55
 
3.8%
c 54
 
3.7%
Other values (13) 368
25.3%

Tech (TF) - Client Server?
Boolean

High correlation  Missing 

Distinct2
Distinct (%)6.2%
Missing52
Missing (%)61.9%
Memory size300.0 B
True
28 
False
 
4
(Missing)
52 
ValueCountFrequency (%)
True 28
33.3%
False 4
 
4.8%
(Missing) 52
61.9%
2025-05-15T14:28:17.910584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Tech (TF) - Client Roles
Categorical

High correlation  Missing 

Distinct10
Distinct (%)38.5%
Missing58
Missing (%)69.0%
Memory size804.0 B
Web/HTML browser;
13 
Web public interface;
Data entry & validation;Data retrieval & presentation;Web/HTML browser;
Run a computer-human interface;Business logic or rule processing;Data entry & validation;Data retrieval & presentation;
Run a computer-human interface;Data entry & validation;Data retrieval & presentation;Web/HTML browser;Security;
 
1
Other values (5)

Length

Max length119
Median length115
Mean length46.269231
Min length17

Characters and Unicode

Total characters1203
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)23.1%

Sample

1st rowData entry & validation;Data retrieval & presentation;Web/HTML browser;
2nd rowWeb public interface;
3rd rowRun a computer-human interface;Data entry & validation;Data retrieval & presentation;Web/HTML browser;Security;
4th rowRun a computer-human interface;Data entry & validation;Web public interface;
5th rowWeb/HTML browser;

Common Values

ValueCountFrequency (%)
Web/HTML browser; 13
 
15.5%
Web public interface; 3
 
3.6%
Data entry & validation;Data retrieval & presentation;Web/HTML browser; 2
 
2.4%
Run a computer-human interface;Business logic or rule processing;Data entry & validation;Data retrieval & presentation; 2
 
2.4%
Run a computer-human interface;Data entry & validation;Data retrieval & presentation;Web/HTML browser;Security; 1
 
1.2%
Run a computer-human interface;Data entry & validation;Web public interface; 1
 
1.2%
Business logic or rule processing;Data entry & validation;Data retrieval & presentation; 1
 
1.2%
Run a computer-human interface;Business logic or rule processing;Database server; 1
 
1.2%
Data entry & validation;Data retrieval & presentation;Device/equipment interface; 1
 
1.2%
Run a computer-human interface;Data entry & validation;Data retrieval & presentation;Web/HTML browser; 1
 
1.2%
(Missing) 58
69.0%

Length

2025-05-15T14:28:18.127462image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:18.377500image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
17
 
12.3%
browser 16
 
11.6%
web/html 13
 
9.4%
entry 9
 
6.5%
validation;data 8
 
5.8%
retrieval 8
 
5.8%
a 6
 
4.3%
run 6
 
4.3%
computer-human 6
 
4.3%
interface 5
 
3.6%
Other values (17) 44
31.9%

Most occurring characters

ValueCountFrequency (%)
e 127
 
10.6%
112
 
9.3%
r 99
 
8.2%
a 94
 
7.8%
t 79
 
6.6%
n 66
 
5.5%
i 64
 
5.3%
o 52
 
4.3%
; 51
 
4.2%
s 47
 
3.9%
Other values (26) 412
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1203
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 127
 
10.6%
112
 
9.3%
r 99
 
8.2%
a 94
 
7.8%
t 79
 
6.6%
n 66
 
5.5%
i 64
 
5.3%
o 52
 
4.3%
; 51
 
4.2%
s 47
 
3.9%
Other values (26) 412
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1203
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 127
 
10.6%
112
 
9.3%
r 99
 
8.2%
a 94
 
7.8%
t 79
 
6.6%
n 66
 
5.5%
i 64
 
5.3%
o 52
 
4.3%
; 51
 
4.2%
s 47
 
3.9%
Other values (26) 412
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1203
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 127
 
10.6%
112
 
9.3%
r 99
 
8.2%
a 94
 
7.8%
t 79
 
6.6%
n 66
 
5.5%
i 64
 
5.3%
o 52
 
4.3%
; 51
 
4.2%
s 47
 
3.9%
Other values (26) 412
34.2%

Tech (TF) - Server Roles
Categorical

High correlation  Missing 

Distinct12
Distinct (%)44.4%
Missing57
Missing (%)67.9%
Memory size804.0 B
Security/authentication;
Database server;HTML/web server;Security/authentication;
HTML/web server;Security/authentication;
Database server;
Database server;HTML/web server;
Other values (7)

Length

Max length157
Median length85
Mean length43.777778
Min length16

Characters and Unicode

Total characters1182
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)25.9%

Sample

1st rowHTML/web server;Security/authentication;
2nd rowMulti-user legacy application;
3rd rowDatabase server;File &/or print server;HTML/web server;Multi-user legacy application;
4th rowDatabase server;HTML/web server;Mail server;Security/authentication;
5th rowSecurity/authentication;

Common Values

ValueCountFrequency (%)
Security/authentication; 6
 
7.1%
Database server;HTML/web server;Security/authentication; 5
 
6.0%
HTML/web server;Security/authentication; 3
 
3.6%
Database server; 3
 
3.6%
Database server;HTML/web server; 3
 
3.6%
Multi-user legacy application; 1
 
1.2%
Database server;File &/or print server;HTML/web server;Multi-user legacy application; 1
 
1.2%
Database server;HTML/web server;Mail server;Security/authentication; 1
 
1.2%
Database server;File &/or print server;HTML/web server;Mail server;Security/authentication; 1
 
1.2%
HTML/web server;Multi-user legacy application; 1
 
1.2%
Other values (2) 2
 
2.4%
(Missing) 57
67.9%

Length

2025-05-15T14:28:18.925238image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
database 15
18.1%
server;html/web 12
14.5%
server;security/authentication 10
12.0%
security/authentication 6
 
7.2%
server 6
 
7.2%
html/web 4
 
4.8%
legacy 3
 
3.6%
application 3
 
3.6%
print 2
 
2.4%
server;multi-user 2
 
2.4%
Other values (17) 20
24.1%

Most occurring characters

ValueCountFrequency (%)
e 156
13.2%
r 102
 
8.6%
t 100
 
8.5%
a 96
 
8.1%
i 72
 
6.1%
s 65
 
5.5%
; 60
 
5.1%
56
 
4.7%
n 46
 
3.9%
c 44
 
3.7%
Other values (31) 385
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 156
13.2%
r 102
 
8.6%
t 100
 
8.5%
a 96
 
8.1%
i 72
 
6.1%
s 65
 
5.5%
; 60
 
5.1%
56
 
4.7%
n 46
 
3.9%
c 44
 
3.7%
Other values (31) 385
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 156
13.2%
r 102
 
8.6%
t 100
 
8.5%
a 96
 
8.1%
i 72
 
6.1%
s 65
 
5.5%
; 60
 
5.1%
56
 
4.7%
n 46
 
3.9%
c 44
 
3.7%
Other values (31) 385
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 156
13.2%
r 102
 
8.6%
t 100
 
8.5%
a 96
 
8.1%
i 72
 
6.1%
s 65
 
5.5%
; 60
 
5.1%
56
 
4.7%
n 46
 
3.9%
c 44
 
3.7%
Other values (31) 385
32.6%

Tech (TF) - Type of Server
Unsupported

Missing  Rejected  Unsupported 

Missing84
Missing (%)100.0%
Memory size804.0 B

Tech (TF) - Web Development
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.9%
Missing50
Missing (%)59.5%
Memory size804.0 B
Web
34 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters102
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeb
2nd rowWeb
3rd rowWeb
4th rowWeb
5th rowWeb

Common Values

ValueCountFrequency (%)
Web 34
40.5%
(Missing) 50
59.5%

Length

2025-05-15T14:28:19.159128image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:19.343439image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
web 34
100.0%

Most occurring characters

ValueCountFrequency (%)
W 34
33.3%
e 34
33.3%
b 34
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 102
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 34
33.3%
e 34
33.3%
b 34
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 102
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 34
33.3%
e 34
33.3%
b 34
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 102
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 34
33.3%
e 34
33.3%
b 34
33.3%

Tech (TF) - DBMS Used
Boolean

Constant  Missing 

Distinct1
Distinct (%)1.8%
Missing27
Missing (%)32.1%
Memory size300.0 B
True
57 
(Missing)
27 
ValueCountFrequency (%)
True 57
67.9%
(Missing) 27
32.1%
2025-05-15T14:28:19.543624image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Tech (TF) - Tools Used
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9880952
Minimum0
Maximum9
Zeros28
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:19.737720image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.916913
Coefficient of variation (CV)0.96419575
Kurtosis1.4674763
Mean1.9880952
Median Absolute Deviation (MAD)2
Skewness1.0686451
Sum167
Variance3.6745554
MonotonicityNot monotonic
2025-05-15T14:28:19.913463image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 32
38.1%
0 28
33.3%
4 14
16.7%
3 2
 
2.4%
7 2
 
2.4%
5 2
 
2.4%
1 2
 
2.4%
6 1
 
1.2%
9 1
 
1.2%
ValueCountFrequency (%)
0 28
33.3%
1 2
 
2.4%
2 32
38.1%
3 2
 
2.4%
4 14
16.7%
5 2
 
2.4%
6 1
 
1.2%
7 2
 
2.4%
9 1
 
1.2%
ValueCountFrequency (%)
9 1
 
1.2%
7 2
 
2.4%
6 1
 
1.2%
5 2
 
2.4%
4 14
16.7%
3 2
 
2.4%
2 32
38.1%
1 2
 
2.4%
0 28
33.3%

People (PRF) - Project user involvement
Unsupported

Missing  Rejected  Unsupported 

Missing84
Missing (%)100.0%
Memory size804.0 B

People (PRF) - BA team experience <1 yr
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct8
Distinct (%)53.3%
Missing69
Missing (%)82.1%
Infinite0
Infinite (%)0.0%
Mean2.4666667
Minimum0
Maximum11
Zeros7
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:20.100121image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile8.9
Maximum11
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.4819261
Coefficient of variation (CV)1.4115917
Kurtosis1.2438937
Mean2.4666667
Median Absolute Deviation (MAD)1
Skewness1.4444557
Sum37
Variance12.12381
MonotonicityNot monotonic
2025-05-15T14:28:20.261005image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 7
 
8.3%
1 2
 
2.4%
3 1
 
1.2%
8 1
 
1.2%
5 1
 
1.2%
6 1
 
1.2%
2 1
 
1.2%
11 1
 
1.2%
(Missing) 69
82.1%
ValueCountFrequency (%)
0 7
8.3%
1 2
 
2.4%
2 1
 
1.2%
3 1
 
1.2%
5 1
 
1.2%
6 1
 
1.2%
8 1
 
1.2%
11 1
 
1.2%
ValueCountFrequency (%)
11 1
 
1.2%
8 1
 
1.2%
6 1
 
1.2%
5 1
 
1.2%
3 1
 
1.2%
2 1
 
1.2%
1 2
 
2.4%
0 7
8.3%

People (PRF) - BA team experience 1 to 3 yr
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct8
Distinct (%)44.4%
Missing66
Missing (%)78.6%
Infinite0
Infinite (%)0.0%
Mean3.7777778
Minimum0
Maximum32
Zeros6
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:20.450542image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q34
95-th percentile11.6
Maximum32
Range32
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.4087036
Coefficient of variation (CV)1.9611274
Kurtosis14.086702
Mean3.7777778
Median Absolute Deviation (MAD)1.5
Skewness3.6058929
Sum68
Variance54.888889
MonotonicityNot monotonic
2025-05-15T14:28:20.670843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 6
 
7.1%
1 3
 
3.6%
5 2
 
2.4%
2 2
 
2.4%
4 2
 
2.4%
3 1
 
1.2%
8 1
 
1.2%
32 1
 
1.2%
(Missing) 66
78.6%
ValueCountFrequency (%)
0 6
7.1%
1 3
3.6%
2 2
 
2.4%
3 1
 
1.2%
4 2
 
2.4%
5 2
 
2.4%
8 1
 
1.2%
32 1
 
1.2%
ValueCountFrequency (%)
32 1
 
1.2%
8 1
 
1.2%
5 2
 
2.4%
4 2
 
2.4%
3 1
 
1.2%
2 2
 
2.4%
1 3
3.6%
0 6
7.1%

People (PRF) - BA team experience >3 yr
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct9
Distinct (%)52.9%
Missing67
Missing (%)79.8%
Infinite0
Infinite (%)0.0%
Mean4.5882353
Minimum0
Maximum25
Zeros3
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:20.898493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile17.8
Maximum25
Range25
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.605479
Coefficient of variation (CV)1.4396557
Kurtosis5.5668843
Mean4.5882353
Median Absolute Deviation (MAD)2
Skewness2.3354237
Sum78
Variance43.632353
MonotonicityNot monotonic
2025-05-15T14:28:21.085804image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 4
 
4.8%
0 3
 
3.6%
2 3
 
3.6%
5 2
 
2.4%
16 1
 
1.2%
3 1
 
1.2%
8 1
 
1.2%
25 1
 
1.2%
6 1
 
1.2%
(Missing) 67
79.8%
ValueCountFrequency (%)
0 3
3.6%
1 4
4.8%
2 3
3.6%
3 1
 
1.2%
5 2
2.4%
6 1
 
1.2%
8 1
 
1.2%
16 1
 
1.2%
25 1
 
1.2%
ValueCountFrequency (%)
25 1
 
1.2%
16 1
 
1.2%
8 1
 
1.2%
6 1
 
1.2%
5 2
2.4%
3 1
 
1.2%
2 3
3.6%
1 4
4.8%
0 3
3.6%

People (PRF) - IT experience <1 yr
Unsupported

Missing  Rejected  Unsupported 

Missing84
Missing (%)100.0%
Memory size804.0 B

People (PRF) - IT experience 1 to 3 yr
Unsupported

Missing  Rejected  Unsupported 

Missing84
Missing (%)100.0%
Memory size804.0 B

People (PRF) - IT experience >3 yr
Unsupported

Missing  Rejected  Unsupported 

Missing84
Missing (%)100.0%
Memory size804.0 B

People (PRF) - IT experience <3 yr
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct8
Distinct (%)34.8%
Missing61
Missing (%)72.6%
Infinite0
Infinite (%)0.0%
Mean3.8695652
Minimum0
Maximum22
Zeros6
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:21.219532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median2
Q35
95-th percentile16.1
Maximum22
Range22
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation5.4880395
Coefficient of variation (CV)1.4182574
Kurtosis5.7485705
Mean3.8695652
Median Absolute Deviation (MAD)2
Skewness2.3559739
Sum89
Variance30.118577
MonotonicityNot monotonic
2025-05-15T14:28:21.477373image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 6
 
7.1%
1 5
 
6.0%
5 5
 
6.0%
2 2
 
2.4%
4 2
 
2.4%
22 1
 
1.2%
8 1
 
1.2%
17 1
 
1.2%
(Missing) 61
72.6%
ValueCountFrequency (%)
0 6
7.1%
1 5
6.0%
2 2
 
2.4%
4 2
 
2.4%
5 5
6.0%
8 1
 
1.2%
17 1
 
1.2%
22 1
 
1.2%
ValueCountFrequency (%)
22 1
 
1.2%
17 1
 
1.2%
8 1
 
1.2%
5 5
6.0%
4 2
 
2.4%
2 2
 
2.4%
1 5
6.0%
0 6
7.1%

People (PRF) - IT experience 3 to 9 yr
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct9
Distinct (%)42.9%
Missing63
Missing (%)75.0%
Infinite0
Infinite (%)0.0%
Mean5.5238095
Minimum0
Maximum33
Zeros3
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:21.690209image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile24
Maximum33
Range33
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.0163523
Coefficient of variation (CV)1.4512362
Kurtosis7.7852361
Mean5.5238095
Median Absolute Deviation (MAD)2
Skewness2.8113271
Sum116
Variance64.261905
MonotonicityNot monotonic
2025-05-15T14:28:21.893409image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 5
 
6.0%
5 3
 
3.6%
0 3
 
3.6%
6 3
 
3.6%
4 3
 
3.6%
3 1
 
1.2%
1 1
 
1.2%
24 1
 
1.2%
33 1
 
1.2%
(Missing) 63
75.0%
ValueCountFrequency (%)
0 3
3.6%
1 1
 
1.2%
2 5
6.0%
3 1
 
1.2%
4 3
3.6%
5 3
3.6%
6 3
3.6%
24 1
 
1.2%
33 1
 
1.2%
ValueCountFrequency (%)
33 1
 
1.2%
24 1
 
1.2%
6 3
3.6%
5 3
3.6%
4 3
3.6%
3 1
 
1.2%
2 5
6.0%
1 1
 
1.2%
0 3
3.6%

People (PRF) - IT experience >9 yr
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct7
Distinct (%)41.2%
Missing67
Missing (%)79.8%
Infinite0
Infinite (%)0.0%
Mean4.3529412
Minimum0
Maximum24
Zeros2
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:22.020091image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile19.2
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.5186384
Coefficient of variation (CV)1.497525
Kurtosis5.678973
Mean4.3529412
Median Absolute Deviation (MAD)2
Skewness2.4915896
Sum74
Variance42.492647
MonotonicityNot monotonic
2025-05-15T14:28:22.248562image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 5
 
6.0%
1 5
 
6.0%
0 2
 
2.4%
2 2
 
2.4%
24 1
 
1.2%
3 1
 
1.2%
18 1
 
1.2%
(Missing) 67
79.8%
ValueCountFrequency (%)
0 2
 
2.4%
1 5
6.0%
2 2
 
2.4%
3 1
 
1.2%
4 5
6.0%
18 1
 
1.2%
24 1
 
1.2%
ValueCountFrequency (%)
24 1
 
1.2%
18 1
 
1.2%
4 5
6.0%
3 1
 
1.2%
2 2
 
2.4%
1 5
6.0%
0 2
 
2.4%

People (PRF) - Project manage experience
Real number (ℝ)

High correlation  Missing 

Distinct12
Distinct (%)63.2%
Missing65
Missing (%)77.4%
Infinite0
Infinite (%)0.0%
Mean22.210526
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:22.449022image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.8
Q13
median8
Q317.5
95-th percentile60.5
Maximum200
Range199
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation45.036502
Coefficient of variation (CV)2.0277098
Kurtosis15.310619
Mean22.210526
Median Absolute Deviation (MAD)5
Skewness3.7890082
Sum422
Variance2028.2865
MonotonicityNot monotonic
2025-05-15T14:28:22.735306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 6
 
7.1%
5 2
 
2.4%
45 2
 
2.4%
1 1
 
1.2%
10 1
 
1.2%
20 1
 
1.2%
200 1
 
1.2%
12 1
 
1.2%
25 1
 
1.2%
8 1
 
1.2%
Other values (2) 2
 
2.4%
(Missing) 65
77.4%
ValueCountFrequency (%)
1 1
 
1.2%
3 6
7.1%
5 2
 
2.4%
8 1
 
1.2%
10 1
 
1.2%
12 1
 
1.2%
13 1
 
1.2%
15 1
 
1.2%
20 1
 
1.2%
25 1
 
1.2%
ValueCountFrequency (%)
200 1
1.2%
45 2
2.4%
25 1
1.2%
20 1
1.2%
15 1
1.2%
13 1
1.2%
12 1
1.2%
10 1
1.2%
8 1
1.2%
5 2
2.4%

People (PRF) - Project manage changes
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)9.4%
Missing52
Missing (%)61.9%
Memory size804.0 B
0.0
27 
1.0
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 27
32.1%
1.0 4
 
4.8%
2.0 1
 
1.2%
(Missing) 52
61.9%

Length

2025-05-15T14:28:23.066782image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:23.227471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 27
84.4%
1.0 4
 
12.5%
2.0 1
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 59
61.5%
. 32
33.3%
1 4
 
4.2%
2 1
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 59
61.5%
. 32
33.3%
1 4
 
4.2%
2 1
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 59
61.5%
. 32
33.3%
1 4
 
4.2%
2 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 59
61.5%
. 32
33.3%
1 4
 
4.2%
2 1
 
1.0%

People (PRF) - Personnel changes
Categorical

High correlation  Missing 

Distinct5
Distinct (%)15.6%
Missing52
Missing (%)61.9%
Memory size804.0 B
0.0
22 
1.0
2.0
 
2
3.0
 
1
12.0
 
1

Length

Max length4
Median length3
Mean length3.03125
Min length3

Characters and Unicode

Total characters97
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)6.2%

Sample

1st row3.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22
26.2%
1.0 6
 
7.1%
2.0 2
 
2.4%
3.0 1
 
1.2%
12.0 1
 
1.2%
(Missing) 52
61.9%

Length

2025-05-15T14:28:23.379883image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:23.627662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22
68.8%
1.0 6
 
18.8%
2.0 2
 
6.2%
3.0 1
 
3.1%
12.0 1
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 54
55.7%
. 32
33.0%
1 7
 
7.2%
2 3
 
3.1%
3 1
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 54
55.7%
. 32
33.0%
1 7
 
7.2%
2 3
 
3.1%
3 1
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 54
55.7%
. 32
33.0%
1 7
 
7.2%
2 3
 
3.1%
3 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 54
55.7%
. 32
33.0%
1 7
 
7.2%
2 3
 
3.1%
3 1
 
1.0%

Project (PRF) - Total project cost
Real number (ℝ)

High correlation  Missing 

Distinct28
Distinct (%)96.6%
Missing55
Missing (%)65.5%
Infinite0
Infinite (%)0.0%
Mean106224.72
Minimum4871
Maximum765000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T14:28:23.850865image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4871
5-th percentile8136.4
Q115680
median57035
Q381500
95-th percentile569800
Maximum765000
Range760129
Interquartile range (IQR)65820

Descriptive statistics

Standard deviation189999.23
Coefficient of variation (CV)1.7886535
Kurtosis9.5003343
Mean106224.72
Median Absolute Deviation (MAD)39875
Skewness3.1729432
Sum3080517
Variance3.6099707 × 1010
MonotonicityNot monotonic
2025-05-15T14:28:24.065252image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
765000 2
 
2.4%
90300 1
 
1.2%
15680 1
 
1.2%
68200 1
 
1.2%
7644 1
 
1.2%
31465 1
 
1.2%
120600 1
 
1.2%
70600 1
 
1.2%
10180 1
 
1.2%
30300 1
 
1.2%
Other values (18) 18
 
21.4%
(Missing) 55
65.5%
ValueCountFrequency (%)
4871 1
1.2%
7644 1
1.2%
8875 1
1.2%
10180 1
1.2%
13880 1
1.2%
14723 1
1.2%
15281 1
1.2%
15680 1
1.2%
17160 1
1.2%
30300 1
1.2%
ValueCountFrequency (%)
765000 2
2.4%
277000 1
1.2%
120600 1
1.2%
104200 1
1.2%
100200 1
1.2%
90300 1
1.2%
81500 1
1.2%
70600 1
1.2%
69850 1
1.2%
68200 1
1.2%

Project (PRF) - Cost currency
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.0%
Missing54
Missing (%)64.3%
Memory size804.0 B
European, euro
16 
Canada, dollar
13 
United States, dollar
 
1

Length

Max length21
Median length14
Mean length14.233333
Min length14

Characters and Unicode

Total characters427
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st rowEuropean, euro
2nd rowEuropean, euro
3rd rowEuropean, euro
4th rowCanada, dollar
5th rowCanada, dollar

Common Values

ValueCountFrequency (%)
European, euro 16
 
19.0%
Canada, dollar 13
 
15.5%
United States, dollar 1
 
1.2%
(Missing) 54
64.3%

Length

2025-05-15T14:28:24.272054image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:24.479172image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
european 16
26.2%
euro 16
26.2%
dollar 14
23.0%
canada 13
21.3%
united 1
 
1.6%
states 1
 
1.6%

Most occurring characters

ValueCountFrequency (%)
a 70
16.4%
r 46
10.8%
o 46
10.8%
e 34
8.0%
u 32
7.5%
31
7.3%
n 30
7.0%
, 30
7.0%
d 28
 
6.6%
l 28
 
6.6%
Other values (8) 52
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 427
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 70
16.4%
r 46
10.8%
o 46
10.8%
e 34
8.0%
u 32
7.5%
31
7.3%
n 30
7.0%
, 30
7.0%
d 28
 
6.6%
l 28
 
6.6%
Other values (8) 52
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 427
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 70
16.4%
r 46
10.8%
o 46
10.8%
e 34
8.0%
u 32
7.5%
31
7.3%
n 30
7.0%
, 30
7.0%
d 28
 
6.6%
l 28
 
6.6%
Other values (8) 52
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 427
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 70
16.4%
r 46
10.8%
o 46
10.8%
e 34
8.0%
u 32
7.5%
31
7.3%
n 30
7.0%
, 30
7.0%
d 28
 
6.6%
l 28
 
6.6%
Other values (8) 52
12.2%

Project (PRF) - Currency multiple
Categorical

High correlation  Missing 

Distinct2
Distinct (%)11.1%
Missing66
Missing (%)78.6%
Memory size804.0 B
No
16 
Yes 10,000

Length

Max length10
Median length2
Mean length2.8888889
Min length2

Characters and Unicode

Total characters52
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 16
 
19.0%
Yes 10,000 2
 
2.4%
(Missing) 66
78.6%

Length

2025-05-15T14:28:24.638430image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:28:24.815433image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
no 16
80.0%
yes 2
 
10.0%
10,000 2
 
10.0%

Most occurring characters

ValueCountFrequency (%)
N 16
30.8%
o 16
30.8%
0 8
15.4%
Y 2
 
3.8%
e 2
 
3.8%
s 2
 
3.8%
2
 
3.8%
1 2
 
3.8%
, 2
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 16
30.8%
o 16
30.8%
0 8
15.4%
Y 2
 
3.8%
e 2
 
3.8%
s 2
 
3.8%
2
 
3.8%
1 2
 
3.8%
, 2
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 16
30.8%
o 16
30.8%
0 8
15.4%
Y 2
 
3.8%
e 2
 
3.8%
s 2
 
3.8%
2
 
3.8%
1 2
 
3.8%
, 2
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 16
30.8%
o 16
30.8%
0 8
15.4%
Y 2
 
3.8%
e 2
 
3.8%
s 2
 
3.8%
2
 
3.8%
1 2
 
3.8%
, 2
 
3.8%

Interactions

2025-05-15T14:27:56.691600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:32.759014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:36.396883image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:40.440288image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-05-15T14:27:09.852905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:14.056023image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:18.940075image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:22.514975image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:26.084692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:30.298289image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:34.226884image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:37.566125image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:40.885504image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:44.409086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:48.927436image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:52.410691image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:56.004305image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:59.929385image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:35.821311image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:39.753621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:45.505526image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:48.899195image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:53.037950image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:57.041784image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:01.728325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:06.411101image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:10.082918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:14.613912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:19.097513image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:22.708669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:26.252742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:30.523040image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:34.442629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:37.706002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:41.035634image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:44.598621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:49.065295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:52.551352image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:56.191772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:28:00.108012image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:35.956720image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:39.917941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:45.691896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:49.028546image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:53.207670image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:57.264381image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:01.932065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:06.621099image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:10.290176image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:14.817969image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:19.254227image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:22.893193image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:26.398471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:30.727338image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:34.576053image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:37.815315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:41.175569image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:44.722454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:49.222752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:52.681438image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:56.367355image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:28:00.285989image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:36.235655image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:40.130670image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:45.863918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:49.190682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:53.349044image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:26:57.505222image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:02.164201image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:06.792462image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:10.457706image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:15.049842image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:19.427619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:23.082648image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:26.536806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:30.991580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:34.723264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:38.067065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:41.386247image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:44.878734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:49.443840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:52.817532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:27:56.538430image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-05-15T14:28:25.175892image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
- CASE Tool UsedExternal (EEF) - Data Quality RatingExternal (EEF) - Industry SectorExternal (EEF) - Organisation TypeISBSG Project IDPeople (PRF) - BA team experience 1 to 3 yrPeople (PRF) - BA team experience <1 yrPeople (PRF) - BA team experience >3 yrPeople (PRF) - IT experience 3 to 9 yrPeople (PRF) - IT experience <3 yrPeople (PRF) - IT experience >9 yrPeople (PRF) - Personnel changesPeople (PRF) - Project manage changesPeople (PRF) - Project manage experienceProcess (PMF) - Development MethodologiesProcess (PMF) - DocsProject (PRF) - Application GroupProject (PRF) - Application TypeProject (PRF) - Cost currencyProject (PRF) - Currency multipleProject (PRF) - Defect DensityProject (PRF) - Development TypeProject (PRF) - Functional SizeProject (PRF) - Manpower Delivery RateProject (PRF) - Max Team SizeProject (PRF) - Normalised Level 1 PDR (ufp)Project (PRF) - Normalised PDR (ufp)Project (PRF) - Normalised Work EffortProject (PRF) - Normalised Work Effort Level 1Project (PRF) - Project Elapsed TimeProject (PRF) - Relative SizeProject (PRF) - Speed of DeliveryProject (PRF) - Team Size GroupProject (PRF) - Total project costProject (PRF) - Year of ProjectTech (TF) - ArchitectureTech (TF) - Client RolesTech (TF) - Client Server?Tech (TF) - Development PlatformTech (TF) - Language TypeTech (TF) - Primary Programming LanguageTech (TF) - Server RolesTech (TF) - Tools Used
- CASE Tool Used1.0000.2310.5960.2110.0000.0000.0000.0000.2040.1700.3400.4120.0000.3340.1820.0000.3340.3780.0001.0000.3510.3800.2670.0000.0000.4120.4120.0000.1160.0000.3950.1010.1200.7070.7650.0000.3270.0000.6120.0000.3750.6670.756
External (EEF) - Data Quality Rating0.2311.0000.4350.6990.0000.0000.0000.0000.5890.4060.0000.2500.0000.0000.8580.6530.0000.8060.8950.7340.0000.2150.8090.9810.5520.1590.1590.5460.3720.4380.7420.7630.5340.7460.4810.3510.5630.5440.2130.1510.2390.3350.800
External (EEF) - Industry Sector0.5960.4351.0000.8990.0000.4400.1480.3690.5050.4350.4720.4960.1920.5800.4030.4930.5020.8400.9620.0000.2640.5840.3860.4420.6530.0000.0000.3930.3820.5550.3760.2780.6150.5890.5470.7320.7960.0000.4510.6800.4850.8080.564
External (EEF) - Organisation Type0.2110.6990.8991.0000.0000.5380.0000.5930.3930.3650.5650.5300.5570.0000.7260.8000.4150.8850.9430.0000.6460.6280.6660.8610.7350.0000.0000.6190.6270.7080.5130.6420.7040.5540.6820.7680.7760.6470.5810.7940.7850.6950.552
ISBSG Project ID0.0000.0000.0000.0001.0000.209-0.2620.0070.2960.334-0.1510.1580.0000.3930.0480.1410.0500.0000.0000.000-0.1740.2230.0990.219-0.051-0.111-0.113-0.028-0.028-0.0240.1370.0620.0000.185-0.1320.0000.0000.0000.0890.0680.0000.0000.131
People (PRF) - BA team experience 1 to 3 yr0.0000.0000.4400.5380.2091.000-0.0460.7730.3660.1340.6070.4050.2430.5380.467-0.0890.0000.4670.0001.0000.1860.0000.188-0.2960.4750.1430.1430.4040.4040.5500.2140.1010.556-1.000-0.3230.0000.5080.3260.2410.9350.7440.471-0.208
People (PRF) - BA team experience <1 yr0.0000.0000.1480.000-0.262-0.0461.000-0.017-0.0090.6630.4620.6130.697-0.0420.358-0.2270.3570.0001.0001.0000.4550.0000.034-0.3590.3100.2410.2410.1170.1170.2010.0000.0310.000NaN-0.3130.0000.2570.2510.0001.0000.3190.118-0.088
People (PRF) - BA team experience >3 yr0.0000.0000.3690.5930.0070.773-0.0171.0000.353-0.1090.6800.4560.6210.1990.525-0.0400.0000.0000.0000.0000.1670.0000.203-0.2390.4470.1330.1330.4700.4360.3530.0000.1250.4480.943-0.1060.0000.5260.0000.3820.0000.5730.408-0.147
People (PRF) - IT experience 3 to 9 yr0.2040.5890.5050.3930.2960.366-0.0090.3531.0000.1010.2290.6170.2640.2130.754-0.1190.0000.5200.0001.000-0.0510.0000.334-0.0810.6580.2840.2840.4730.4730.1350.5040.3700.736-0.147-0.6000.0000.4740.1870.0001.0000.2690.6230.058
People (PRF) - IT experience <3 yr0.1700.4060.4350.3650.3340.1340.663-0.1090.1011.0000.1020.4770.0000.0720.655-0.0070.0000.4630.2041.0000.2330.2790.2590.2730.233-0.079-0.0790.0940.105-0.1600.3290.3870.692-0.073-0.5110.4150.4080.2790.4010.9000.4810.4630.223
People (PRF) - IT experience >9 yr0.3400.0000.4720.565-0.1510.6070.4620.6800.2290.1021.0000.5870.7110.1990.745-0.1040.0000.1070.0000.0000.4510.1570.123-0.4830.5270.2110.2110.3590.3410.4770.000-0.0390.7600.943-0.3010.0000.6100.0000.3440.0000.4730.573-0.300
People (PRF) - Personnel changes0.4120.2500.4960.5300.1580.4050.6130.4560.6170.4770.5871.0000.5500.1600.6380.0000.1250.5390.7230.0000.0000.4380.6100.0000.6450.4000.4000.6100.5130.3610.5370.3460.5990.9610.2860.1170.6030.0000.1680.4190.2810.4760.594
People (PRF) - Project manage changes0.0000.0000.1920.5570.0000.2430.6970.6210.2640.0000.7110.5501.0000.0000.6740.1200.2480.3591.0001.0000.0000.4020.4950.0000.4960.0800.0800.4950.5020.5150.0000.0000.4541.0000.0000.0000.7470.0000.0000.0000.0000.4980.245
People (PRF) - Project manage experience0.3340.0000.5800.0000.3930.538-0.0420.1990.2130.0720.1990.1600.0001.0000.000-0.0660.7700.5770.5000.707-0.0170.5020.3420.3680.136-0.292-0.2920.1570.152-0.0520.0000.2750.0000.778-0.2950.0500.7070.0000.1700.7010.1710.615-0.305
Process (PMF) - Development Methodologies0.1820.8580.4030.7260.0480.4670.3580.5250.7540.6550.7450.6380.6740.0001.0000.4900.0000.7960.9110.0000.1270.3130.8720.9380.8520.1700.1700.8640.8270.5160.5920.7220.6390.5780.4540.4460.6290.2830.5560.0000.2180.6610.588
Process (PMF) - Docs0.0000.6530.4930.8000.141-0.089-0.227-0.040-0.119-0.007-0.1040.0000.120-0.0660.4901.0000.0000.8330.9140.9350.6140.5020.3770.6340.6150.0190.0300.1790.1640.3060.395-0.0570.505-0.430-0.5980.4450.7660.5990.2020.6100.4310.3530.832
Project (PRF) - Application Group0.3340.0000.5020.4150.0500.0000.3570.0000.0000.0000.0000.1250.2480.7700.0000.0001.0000.8250.0000.6970.0000.3290.0000.0000.0000.0000.0000.1730.3590.3530.1970.0000.0000.9620.4680.4080.5860.0000.0000.1210.2330.7580.714
Project (PRF) - Application Type0.3780.8060.8400.8850.0000.4670.0000.0000.5200.4630.1070.5390.3590.5770.7960.8330.8251.0000.9230.9350.6020.7300.7730.8280.7900.0000.0000.7470.7380.7350.5800.5840.7380.7890.7190.7750.9010.6830.7750.8010.7130.8420.833
Project (PRF) - Cost currency0.0000.8950.9620.9430.0000.0001.0000.0000.0000.2040.0000.7231.0000.5000.9110.9140.0000.9231.0000.0000.6550.5840.6980.9640.0000.0000.0001.0001.0000.5260.8080.7160.9200.7470.9430.3550.0000.6031.0000.9820.7980.9130.943
Project (PRF) - Currency multiple1.0000.7340.0000.0000.0001.0001.0000.0001.0001.0000.0000.0001.0000.7070.0000.9350.6970.9350.0001.0001.0000.7340.0000.0000.0000.0000.0001.0001.0000.7340.5930.0000.7070.9310.9010.7070.0000.0001.0000.0000.0880.0000.935
Project (PRF) - Defect Density0.3510.0000.2640.646-0.1740.1860.4550.167-0.0510.2330.4510.0000.000-0.0170.1270.6140.0000.6020.6551.0001.0000.3710.214-0.057-0.049-0.206-0.2050.0180.0170.5820.000-0.2620.000-0.038-0.3710.0000.0000.4360.1070.5170.3890.0000.395
Project (PRF) - Development Type0.3800.2150.5840.6280.2230.0000.0000.0000.0000.2790.1570.4380.4020.5020.3130.5020.3290.7300.5840.7340.3711.0000.0590.0000.0000.0000.0000.0000.0000.5570.5330.0000.4090.4350.5300.4390.7540.0000.3240.3340.4770.7470.452
Project (PRF) - Functional Size0.2670.8090.3860.6660.0990.1880.0340.2030.3340.2590.1230.6100.4950.3420.8720.3770.0000.7730.6980.0000.2140.0591.0000.7320.806-0.085-0.0920.6330.609-0.2090.7390.7870.712-0.140-0.6100.1670.6810.3360.0000.0000.0000.5040.174
Project (PRF) - Manpower Delivery Rate0.0000.9810.4420.8610.219-0.296-0.359-0.239-0.0810.273-0.4830.0000.0000.3680.9380.6340.0000.8280.9640.000-0.0570.0000.7321.0000.414-0.188-0.1830.4780.387-0.7960.9530.8640.0000.644-0.3860.0001.0000.1600.0000.0000.0001.0000.287
Project (PRF) - Max Team Size0.0000.5520.6530.735-0.0510.4750.3100.4470.6580.2330.5270.6450.4960.1360.8520.6150.0000.7900.0000.000-0.0490.0000.8060.4141.0000.6350.6360.9000.866-0.3060.4900.7370.822-0.425-0.8090.4220.7090.0000.6820.2570.3720.5530.387
Project (PRF) - Normalised Level 1 PDR (ufp)0.4120.1590.0000.000-0.1110.1430.2410.1330.284-0.0790.2110.4000.080-0.2920.1700.0190.0000.0000.0000.000-0.2060.000-0.085-0.1880.6351.0000.9980.6530.683-0.1280.193-0.0110.5310.277-0.0200.4590.6150.0000.6640.2430.2990.5100.147
Project (PRF) - Normalised PDR (ufp)0.4120.1590.0000.000-0.1130.1430.2410.1330.284-0.0790.2110.4000.080-0.2920.1700.0300.0000.0000.0000.000-0.2050.000-0.092-0.1830.6360.9981.0000.6540.675-0.1310.193-0.0160.5310.277-0.0010.4590.6150.0000.6640.2430.2990.5100.144
Project (PRF) - Normalised Work Effort0.0000.5460.3930.619-0.0280.4040.1170.4700.4730.0940.3590.6100.4950.1570.8640.1790.1730.7471.0001.0000.0180.0000.6330.4780.9000.6530.6541.0000.990-0.2720.4970.5720.7440.665-0.3650.4900.6310.0000.4500.0000.1940.4030.092
Project (PRF) - Normalised Work Effort Level 10.1160.3720.3820.627-0.0280.4040.1170.4360.4730.1050.3410.5130.5020.1520.8270.1640.3590.7381.0001.0000.0170.0000.6090.3870.8660.6830.6750.9901.000-0.2410.6120.5450.7170.651-0.3910.4370.6960.0000.4340.0000.1240.4890.122
Project (PRF) - Project Elapsed Time0.0000.4380.5550.708-0.0240.5500.2010.3530.135-0.1600.4770.3610.515-0.0520.5160.3060.3530.7350.5260.7340.5820.557-0.209-0.796-0.306-0.128-0.131-0.272-0.2411.0000.406-0.6870.598-0.346-0.2290.6820.7240.1700.5980.4830.4810.7190.455
Project (PRF) - Relative Size0.3950.7420.3760.5130.1370.2140.0000.0000.5040.3290.0000.5370.0000.0000.5920.3950.1970.5800.8080.5930.0000.5330.7390.9530.4900.1930.1930.4970.6120.4061.0000.7020.5650.7860.4800.4950.4800.5780.2130.2840.2250.5100.543
Project (PRF) - Speed of Delivery0.1010.7630.2780.6420.0620.1010.0310.1250.3700.387-0.0390.3460.0000.2750.722-0.0570.0000.5840.7160.000-0.2620.0000.7870.8640.737-0.011-0.0160.5720.545-0.6870.7021.0000.6330.225-0.2170.0000.0000.4050.0000.1050.0000.000-0.192
Project (PRF) - Team Size Group0.1200.5340.6150.7040.0000.5560.0000.4480.7360.6920.7600.5990.4540.0000.6390.5050.0000.7380.9200.7070.0000.4090.7120.0000.8220.5310.5310.7440.7170.5980.5650.6331.0000.9570.5150.6180.6290.3450.5720.4000.4690.4920.563
Project (PRF) - Total project cost0.7070.7460.5890.5540.185-1.000NaN0.943-0.147-0.0730.9430.9611.0000.7780.578-0.4300.9620.7890.7470.931-0.0380.435-0.1400.644-0.4250.2770.2770.6650.651-0.3460.7860.2250.9571.0000.6870.5951.0000.5951.0000.4270.7140.953-0.519
Project (PRF) - Year of Project0.7650.4810.5470.682-0.132-0.323-0.313-0.106-0.600-0.511-0.3010.2860.000-0.2950.454-0.5980.4680.7190.9430.901-0.3710.530-0.610-0.386-0.809-0.020-0.001-0.365-0.391-0.2290.480-0.2170.5150.6871.0000.5140.6830.3590.4730.7700.4880.655-0.539
Tech (TF) - Architecture0.0000.3510.7320.7680.0000.0000.0000.0000.0000.4150.0000.1170.0000.0500.4460.4450.4080.7750.3550.7070.0000.4390.1670.0000.4220.4590.4590.4900.4370.6820.4950.0000.6180.5950.5141.0000.7440.9830.3870.3170.6010.6160.499
Tech (TF) - Client Roles0.3270.5630.7960.7760.0000.5080.2570.5260.4740.4080.6100.6030.7470.7070.6290.7660.5860.9010.0000.0000.0000.7540.6811.0000.7090.6150.6150.6310.6960.7240.4800.0000.6291.0000.6830.7441.0001.0000.7780.0490.6840.8090.705
Tech (TF) - Client Server?0.0000.5440.0000.6470.0000.3260.2510.0000.1870.2790.0000.0000.0000.0000.2830.5990.0000.6830.6030.0000.4360.0000.3360.1600.0000.0000.0000.0000.0000.1700.5780.4050.3450.5950.3590.9831.0001.0000.0000.0000.5551.0000.314
Tech (TF) - Development Platform0.6120.2130.4510.5810.0890.2410.0000.3820.0000.4010.3440.1680.0000.1700.5560.2020.0000.7751.0001.0000.1070.3240.0000.0000.6820.6640.6640.4500.4340.5980.2130.0000.5721.0000.4730.3870.7780.0001.0000.7730.8590.7390.467
Tech (TF) - Language Type0.0000.1510.6800.7940.0680.9351.0000.0001.0000.9000.0000.4190.0000.7010.0000.6100.1210.8010.9820.0000.5170.3340.0000.0000.2570.2430.2430.0000.0000.4830.2840.1050.4000.4270.7700.3170.0490.0000.7731.0000.9620.5830.544
Tech (TF) - Primary Programming Language0.3750.2390.4850.7850.0000.7440.3190.5730.2690.4810.4730.2810.0000.1710.2180.4310.2330.7130.7980.0880.3890.4770.0000.0000.3720.2990.2990.1940.1240.4810.2250.0000.4690.7140.4880.6010.6840.5550.8590.9621.0000.6670.338
Tech (TF) - Server Roles0.6670.3350.8080.6950.0000.4710.1180.4080.6230.4630.5730.4760.4980.6150.6610.3530.7580.8420.9130.0000.0000.7470.5041.0000.5530.5100.5100.4030.4890.7190.5100.0000.4920.9530.6550.6160.8091.0000.7390.5830.6671.0000.714
Tech (TF) - Tools Used0.7560.8000.5640.5520.131-0.208-0.088-0.1470.0580.223-0.3000.5940.245-0.3050.5880.8320.7140.8330.9430.9350.3950.4520.1740.2870.3870.1470.1440.0920.1220.4550.543-0.1920.563-0.519-0.5390.4990.7050.3140.4670.5440.3380.7141.000

Missing values

2025-05-15T14:28:00.721703image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-15T14:28:01.770720image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-15T14:28:03.305687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ISBSG Project IDExternal (EEF) - Data Quality RatingProject (PRF) - Year of ProjectExternal (EEF) - Industry SectorExternal (EEF) - Organisation TypeProject (PRF) - Application GroupProject (PRF) - Application TypeProject (PRF) - Development TypeTech (TF) - Development PlatformTech (TF) - Language TypeTech (TF) - Primary Programming LanguageProject (PRF) - Functional SizeProject (PRF) - Relative SizeProject (PRF) - Normalised Work Effort Level 1Project (PRF) - Normalised Work EffortProject (PRF) - Normalised Level 1 PDR (ufp)Project (PRF) - Normalised PDR (ufp)Project (PRF) - Defect DensityProject (PRF) - Speed of DeliveryProject (PRF) - Manpower Delivery RateProject (PRF) - Project Elapsed TimeProject (PRF) - Team Size GroupProject (PRF) - Max Team Size- CASE Tool UsedProcess (PMF) - Development MethodologiesProcess (PMF) - Prototyping UsedProcess (PMF) - DocsTech (TF) - ArchitectureTech (TF) - Client Server?Tech (TF) - Client RolesTech (TF) - Server RolesTech (TF) - Type of ServerTech (TF) - Web DevelopmentTech (TF) - DBMS UsedTech (TF) - Tools UsedPeople (PRF) - Project user involvementPeople (PRF) - BA team experience <1 yrPeople (PRF) - BA team experience 1 to 3 yrPeople (PRF) - BA team experience >3 yrPeople (PRF) - IT experience <1 yrPeople (PRF) - IT experience 1 to 3 yrPeople (PRF) - IT experience >3 yrPeople (PRF) - IT experience <3 yrPeople (PRF) - IT experience 3 to 9 yrPeople (PRF) - IT experience >9 yrPeople (PRF) - Project manage experiencePeople (PRF) - Project manage changesPeople (PRF) - Personnel changesProject (PRF) - Total project costProject (PRF) - Cost currencyProject (PRF) - Currency multiple
010279B2013BankingGovernment;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;Business ApplicationSurveillance and security;EnhancementPC3GLC#26.0XS52522.02.0NaN2.22.212.011.0NaNAgile Development;NaN6Stand aloneNaNNaNNaNNaNNaNYes2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
110317B2015GovernmentGovernment;Business ApplicationBusiness Application;EnhancementNaN4GL.Net8.0XXS816816102.0102.00.07.3NaN1.1NaNNaNNaNAgile Development;NaN3NaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN81500.0European, euroNo
210572B2014GovernmentGovernment;Business ApplicationBusiness Application;EnhancementNaN4GLOracle85.0S7747749.19.10.0212.5NaN0.4NaNNaNNaNAgile Development;NaN3NaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN69850.0European, euroNo
311278A2010Service IndustryCommunity Services;Business ApplicationWorkflow support & management;Complex process control;EnhancementMulti3GLJavaScript89.0S4036403645.345.356.217.82.05.09-149.0NoAgile Development;NaN16Client serverYesData entry & validation;Data retrieval & presentation;Web/HTML browser;HTML/web server;Security/authentication;NaNNaNYes2NaN3.01.05.0NaNNaNNaN0.05.04.03.00.03.0NaNNaNNaN
411497B2012BankingGovernment;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;Business ApplicationSurveillance and security;EnhancementPC3GLC#31.0S88882.82.8NaN2.62.612.011.0NaNAgile Development;NaN6Stand aloneNaNNaNNaNNaNNaNYes2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
511509B2012CommunicationTelecommunications;Real-Time ApplicationOnline analysis and reporting;Embedded software - simple device control;Telecom & network management;EnhancementMulti3GLC++204.0M17805780538.338.30.029.13.67.05-88.0NaNNaNNaN6Multi-tierNaNNaNNaNNaNNaNYes2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
611738B2012BankingGovernment;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;Business ApplicationSurveillance and security;EnhancementPC3GLC#10.0XS29292.92.9NaN0.80.812.011.0NaNAgile Development;NaN6Stand aloneNaNNaNNaNNaNNaNYes2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
711801A2009GovernmentGovernment;NaNIdM;EnhancementMulti3GLC#63.0S2742274243.543.547.66.30.810.05-88.0NaNAgile Development;NaN18Multi-tier with web public interfaceYesWeb public interface;Multi-user legacy application;NaNWebYes3NaN8.00.00.0NaNNaNNaN2.02.04.01.01.00.0NaNNaNNaN
812664B2014GovernmentGovernment;Business ApplicationBusiness Application;EnhancementNaN4GLOracle138.0M16826824.94.90.0345.0NaN0.4NaNNaNNaNAgile Development;NaN3NaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN61435.0European, euroNo
913026B2013BankingGovernment;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;Business ApplicationSurveillance and security;EnhancementPC3GLC#5.0XXS27275.45.4NaN0.40.412.011.0NaNAgile Development;NaN6Stand aloneNaNNaNNaNNaNNaNYes2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
ISBSG Project IDExternal (EEF) - Data Quality RatingProject (PRF) - Year of ProjectExternal (EEF) - Industry SectorExternal (EEF) - Organisation TypeProject (PRF) - Application GroupProject (PRF) - Application TypeProject (PRF) - Development TypeTech (TF) - Development PlatformTech (TF) - Language TypeTech (TF) - Primary Programming LanguageProject (PRF) - Functional SizeProject (PRF) - Relative SizeProject (PRF) - Normalised Work Effort Level 1Project (PRF) - Normalised Work EffortProject (PRF) - Normalised Level 1 PDR (ufp)Project (PRF) - Normalised PDR (ufp)Project (PRF) - Defect DensityProject (PRF) - Speed of DeliveryProject (PRF) - Manpower Delivery RateProject (PRF) - Project Elapsed TimeProject (PRF) - Team Size GroupProject (PRF) - Max Team Size- CASE Tool UsedProcess (PMF) - Development MethodologiesProcess (PMF) - Prototyping UsedProcess (PMF) - DocsTech (TF) - ArchitectureTech (TF) - Client Server?Tech (TF) - Client RolesTech (TF) - Server RolesTech (TF) - Type of ServerTech (TF) - Web DevelopmentTech (TF) - DBMS UsedTech (TF) - Tools UsedPeople (PRF) - Project user involvementPeople (PRF) - BA team experience <1 yrPeople (PRF) - BA team experience 1 to 3 yrPeople (PRF) - BA team experience >3 yrPeople (PRF) - IT experience <1 yrPeople (PRF) - IT experience 1 to 3 yrPeople (PRF) - IT experience >3 yrPeople (PRF) - IT experience <3 yrPeople (PRF) - IT experience 3 to 9 yrPeople (PRF) - IT experience >9 yrPeople (PRF) - Project manage experiencePeople (PRF) - Project manage changesPeople (PRF) - Personnel changesProject (PRF) - Total project costProject (PRF) - Cost currencyProject (PRF) - Currency multiple
7430029B2012BankingGovernment;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;Business ApplicationSurveillance and security;EnhancementPC3GLC#72.0S2252253.13.1NaN6.03.012.022.0NaNAgile Development;NaN6Stand aloneNaNNaNNaNNaNNaNYes2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7530367A2009Service IndustryGovernment;Community Services;Business ApplicationCatalogue/register of things or events;EnhancementMulti3GLPL/I117.0M13549354930.330.30.016.71.77.09-1410.0NoAgile Development;Yes17Client serverYesNaNNaNNaNNaNYes2NaN0.04.06.0NaNNaNNaN0.06.04.013.00.00.0NaNNaNNaN
7630466B2014GovernmentGovernment;Business ApplicationBusiness Application;EnhancementNaN4GL.Net4.0XXS684684171.0171.00.05.7NaN0.7NaNNaNNaNAgile Development;NaN3NaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN68200.0European, euroNo
7730621B2013BankingGovernment;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;Business ApplicationSurveillance and security;EnhancementPC3GLC#13.0XS47473.63.6NaN1.11.112.011.0NaNAgile Development;NaN6Stand aloneNaNNaNNaNNaNNaNYes2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7830658A2009EducationEducation Institution;Electricity, Gas, Water;IEEE;Business ApplicationContent management system;Dynamic website;New DevelopmentPC3GLJava160.0M17847844.94.9NaN53.310.73.05-85.0NaNAgile Development;Personal Software Process (PSP);Unified Process;NaN18Multi-tier with web public interfaceYesWeb/HTML browser;Security/authentication;NaNWebYes4NaNNaNNaNNaNNaNNaNNaN5.0NaNNaNNaN0.00.015680.0Canada, dollarNaN
7931103B2012BankingGovernment;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;Business ApplicationSurveillance and security;EnhancementPC3GLC#11.0XS43433.93.9NaN0.90.912.011.0NaNAgile Development;NaN6Stand aloneNaNNaNNaNNaNNaNYes2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8031166A2011GovernmentGovernment;Infrastructure SoftwareData or database management;New DevelopmentPC4GLOracle492.0M2102011052.12.2NaN164.082.03.022.0YesAgile Development;NaN17Multi-tier with web public interfaceYesWeb/HTML browser;Database server;HTML/web server;NaNWebYes1NaNNaNNaN2.0NaNNaNNaNNaNNaN2.045.00.01.0765000.0European, euroYes 10,000
8131166A2011GovernmentGovernment;Infrastructure SoftwareData or database management;New DevelopmentPC4GLOracle422.0M2102011052.42.6NaN140.770.33.022.0YesAgile Development;NaN18Multi-tier with web public interfaceYesWeb/HTML browser;Database server;HTML/web server;NaNWebYes1NaNNaNNaN2.0NaNNaNNaNNaNNaN2.045.00.01.0765000.0European, euroYes 10,000
8231969B2013BankingGovernment;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;Business ApplicationSurveillance and security;EnhancementPC3GLC#15.0XS47473.13.1NaN1.31.312.011.0NaNAgile Development;NaN6Stand aloneNaNNaNNaNNaNNaNYes2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8332010D2013ManufacturingGovernment;Real Estate & Property;Education Institution;Manufacturing;Construction;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Community Services;Defence;Financial, Property & Business Services;Banking;ProfessionalBusiness ApplicationCatalogue/register of things or events;Customer billing;Financial transaction process/accounting;Job, case, incident, project management;Management or performance reporting;Online analysis and reporting;Workflow support & management;Mathematical modellingNew DevelopmentPC3GLC#5393.0XL4004600.10.11.92157.22157.22.511.0YesAgile Development;Extreme Programming (XP);Iterative;Rapid Application Development (RAD);Scrum;Timeboxing;Unified Process;IMES OOM;Yes20Stand aloneNoNaNNaNNaNWebYes7NaN0.00.01.0NaNNaNNaN0.00.01.03.00.00.0277000.0United States, dollarNo